Search Results (33)

Objective: This paper develops a conceptual theory-building framework for understanding and managing AI-enabled workplace conflict. It reframes AI-mediated conflict as a socio-technical justice problem and proposes the Hybrid Conflict Governance Model (HCGM), which links HR policy design, ethical governance mechanisms, and workforce capability development to conflict outcomes through procedural justice, trust, contestability, and human oversight pathways. Methods: This study adopts a conceptual theory-building design based on interdisciplinary literature synthesis. Literature from human resource management, artificial intelligence governance, organizational justice, socio-technical systems theory, digital work, and workplace conflict was synthesized using a problem-driven conceptual approach. The study integrates these literature streams to develop a theoretical framework explaining how AI-enabled workplace conflict emerges and how governance mechanisms may influence conflict outcomes. Conceptual Findings: The analysis identifies four major AI-enabled conflict dynamics: algorithmic bias, algorithmic surveillance, human–AI decision misalignment, and opacity in AI-supported decision making. The proposed HCGM explains how AI system characteristics influence employee justice perceptions, trust, and conflict escalation or reduction. The model identifies four interdependent governance layers: foundational rights, structural governance, operational integration, and capability development. These layers interact dynamically and are shaped by boundary conditions including organizational culture, AI autonomy, workforce digital literacy, and regulatory context. Conclusion: AI-enabled workplace conflict cannot be managed through technical controls alone. Effective governance requires integrated HR policies, ethical oversight, transparent decision processes, human review mechanisms, and workforce capability development. The HCGM contributes to AI governance, HRM, and workplace conflict literature by explaining how socio-technical governance mechanisms shape fairness perceptions, trust, and conflict outcomes in AI-mediated workplaces. Full article

The agricultural industry is under increasing pressure to become sustainable in the face of the escalating environmental concerns of today’s world. This study on the agricultural sector of Cameroon examined the interplay of green human resource management (GHRM), pro-environmental behavior (PEB), green psychological empowerment (GPE) and interpersonal cynicism (IC). Questionnaires were distributed through Google Forms to 500 staff members, of which 411 were returned. To analyze the data, we used the PLS SEM technique (algorithm, bootstrapping, and blindfolding). GHRM was found to foster PEB and GPE by enhancing their environmental awareness and capabilities. The link between green HRM and PEB showed partial mediation by green psychological empowerment, which is an indicator of purpose, competence, and autonomy in environmental sustainability. Moreover, it was hypothesized that interpersonal cynicism would moderate the relationship between GHRM and GPE. The moderating effect, however, was negative and not statistically significant. This implies that the strength of the relationship is not significantly affected by interpersonal cynicism. This shows that GHRM still has a positive influence on the green psychological empowerment of employees regardless of the degree of interpersonal cynicism. Therefore, this study offers actionable insight for developing GHRM strategies that enhance empowerment while reducing interpersonal cynicism in the workplace, thereby boosting the practice of sustainability in the agricultural domain. Full article

Background: Acute myocardial infarction (AMI) remains the most lethal critical emergency worldwide. Although Angong Niuhuang Pill (ANP) is an established rescue medicine that has demonstrated outstanding therapeutic potential for cardiovascular diseases, its modern molecular mechanism has never been systematically elucidated because of its chemical complexity and unidentified targets. Methods: This study utilizes a multi-layer analytical pipeline of AI mining, network pharmacology, transcriptomics, and experimental confirmation. The components of ANP were comprehensively identified by UHPLC-Q Exactive Orbitrap HRMS. The TranSiGen algorithm was utilized to deeply mine the data and rank the components according to their relevance to AMI. The top 20 components were selected as prior weights and introduced into network pharmacology for analysis. Subsequently, a mouse model of AMI was established by ligating the left coronary artery. Cardiac function in the mice was evaluated by echocardiography and serum biochemical indicators. The pathological changes in the heart tissue were assessed by hematoxylin-eosin (H&E) and Masson staining. Cardiac transcriptome sequencing was performed, and pathway enrichment was analyzed by KEGG. The key pathways were verified by qPCR and immunofluorescence, achieving cross-validation between AI prediction and experimental findings. Results: The identification of ANP resulted in the detection of a total of 73 compounds, and the TranSiGen algorithm was employed to prioritize these compounds, yielding a ranked list of the top 20 candidates. Functional evaluation using echocardiography, serum biochemical markers, and histopathological examination demonstrated that ANP significantly ameliorated cardiac function in mice following myocardial infarction. Integration of network pharmacology and transcriptomic enrichment identified convergent axes of IL-17 signaling and mitochondrial quality control, which were subsequently experimentally validated as mechanisms by which ANP ameliorated cardiac injury. Experimental validation confirmed that ANP downregulated protein expression of IL-17A and TNF-α, normalized PINK1 and LC3-II/LC3-I marker profiles, with concomitant p62 reduction, thereby providing comprehensive molecular evidence at both transcriptional and translational levels to support the AI-driven predictions. Conclusions: This study identified IL-17 signaling and mitochondrial quality control as pathway axes associated with ANP-mediated cardioprotection against AMI, supported by AI-driven compound screening, transcriptome-network cross-validation, and experimental confirmation. This analytical framework may be adaptable to other complex TCM formulas for mechanism exploration and clinical translation. Full article

The growing diversity of anthropogenic chemicals in the environment far exceeds the scope of routine analytical monitoring. Non-target screening (NTS) using high-resolution mass spectrometry (HRMS) has thus emerged to discover unknown organic contaminants. Liquid or gas chromatography (LC/GC) coupled with ion mobility–mass spectrometry (IM-MS) further enhances NTS by providing multidimensional, structurally informative data. Machine learning (ML) offers a powerful solution by efficiently processing high-dimensional data and uncovering patterns. Both supervised and unsupervised learning approaches show strong potential to streamline labor-intensive processes. This review provides an overview of key ML algorithms and representative workflows in LC/GC-(IM-) MS-related NTS, followed by a critical synthesis of recent advances in ML-enabled applications across the entire NTS procedure, from sample analysis to data acquisition, and ultimately risk assessment. Continued advances in ML are expected to transform NTS into a more efficient and robust tool for risk assessment. Full article

Background: Untargeted metabolomics enables comprehensive profiling of biological systems, but accurate metabolite annotation remains a critical bottleneck due to incomplete spectral libraries and structural isomerism. The use of in silico annotation tools can increase the coverage of annotated compounds, but it remains unclear whether these tools, in the absence of reference standards, can reliably annotate real-world experimental LC-HRMS data and whether they are sufficient for this task. Methods: This study assesses the performance and limitations of four widely used in silico structure prediction tools (MassFrontier, MetFrag, MS-FINDER, and SIRIUS/CSI:FingerID) when applied to an experimentally acquired feature set previously used to differentiate patients with depressive disorders from healthy controls. To ensure uniform evaluation across tools under realistic but optimized conditions, the quality of MS/MS data was improved using a parallel reaction monitoring method, allowing acquisition of interpretable fragmentation spectra for 26 of the 28 detected features. Results: For most features, all tools were able to suggest structure candidates. However, none of the tools proved sufficient as a standalone solution for reliable metabolite annotation. Due to their different algorithms, each tool had strengths and weaknesses in fragmentation interpretation, candidate generation, and ranking, resulting in incomplete or inconsistent annotations. While the combined application of all four tools provided a substantial improvement in putative annotation over conventional spectral library matching, the in silico structure prediction tools often prioritized chemically implausible, biologically irrelevant, or artifactual candidates. Consequently, manual expert evaluation was required to assess the chemical plausibility and biological relevance of the proposed structures. This ultimately reduced the number of biologically plausible metabolites putatively associated with disease to ten. Conclusions: Overall, these results demonstrate that existing in silico annotation tools can substantially support the annotation of experimental metabolomics data, but are insufficient on their own. Reliable identification of metabolites in complex biological matrices still depends on high-quality MS/MS data acquisition, the combined use of complementary tools, and mandatory post-annotation expert curation. Full article

Artificial intelligence (AI) is increasingly reshaping organizational dynamics, not only through efficiency gains but by influencing how work is structured, interpreted, and experienced. In healthcare, where professional team stability is crucial, this transformation intersects with structural issues such as persistent nurse turnover. This study presents an exploratory case study of a private accredited hospital in Italy that introduced an AI-enabled shift scheduling system (“Dream-Shift”) in response to perceived inequities and workforce instability. The system was embedded in a participatory architecture that included a Nursing Practice Council and HR dashboards to visualize staffing patterns. Drawing on theories of Sustainable Human Resource Management (SHRM), algorithmic management, and people sustainability, the study examines how AI-mediated transparency and participation affect fairness perceptions, predictability, and organizational climate. Using administrative data, ethnographic observations, internal documents, and informal feedback, the study finds that the algorithm did not eliminate all inequities but made decision constraints visible and debatable. It redistributed the emotional burden of scheduling and enabled more structured conversations about work. Managers transitioned from unilateral decision-makers to facilitators of collective interpretation. The results suggest that when integrated into participatory infrastructures, AI can foster organizational transparency, support relational stability, and act as a socio-technical enabler of people sustainability rather than as a tool of control. Full article

We aimed to establish a rapid and precise method for identifying and quantifying multi-species vegetable oil (corn oil, olive oil (OLO), soybean oil, and sunflower oil (SUO)) adulterations in camellia oil (CAO), using soft ionization by chemical reaction in transfer–high-resolution mass spectrometry (SICRIT-HRMS) and machine learning methods. The results showed that SICRIT-HRMS could effectively characterize the volatile profiles of pure and adulterated CAO samples, including binary, ternary, quaternary, and quinary adulteration systems. The low m/z region (especially 100–300) exhibited importance to oil classification in multiple feature-selection methods. For qualitative detection, binary classification models based on convolutional neural networks (CNN), Random Forest (RF), and gradient boosting trees (GBT) algorithms showed high accuracies (98.70–100.00%) for identifying CAO adulteration under no dimensionality reduction (NON), principal component analysis (PCA), and uniform manifold approximation and projection (UMAP) strategies. The RF algorithm exhibited relatively high accuracy (96.25–99.45%) in multiclass classification. Moreover, the five models, including CNN, RF, support vector machines (SVM), logistic regression (LR), and GBT, exhibited different performances in distinguishing pure and adulterated CAO. Among 1093 blind oil samples, under NON, PCA, and UMAP: 10, 5, and 67 samples were misclassified by CNN model; 6, 7, and 41 samples were misclassified by RF model; 8, 9, and 82 samples were misclassified by SVM model; 17, 18, and 78 samples were misclassified by LR model; 7, 9, and 43 samples were misclassified by GBT model. For quantitative prediction, the PCA-CNN model performed optimally in predicting adulteration levels in CAO, especially with respect to OLO and SUO, exhibiting a high coefficient of determination for calibration ($R_C^2$, 0.9664–0.9974) and coefficient of determination for prediction ($R_p^2$, 0.9599–0.9963) values, low root mean square error of calibration (RMSEC, 0.9–5.3%) and root mean square error of prediction (RMSEP, 1.1–5.8%) values, and RPD (5.0–16.3) values greater than 3.0. These results indicate that SICRIT-HRMS combined with machine learning can rapidly and accurately identify and quantify multi-species vegetable oil adulterations in CAO, which provides a reference for developing non-targeted and high-throughput detection methods in edible oil authenticity. Full article

Background/Objectives: Thiopurines remain a cornerstone in the management of inflammatory bowel disease (IBD) and gastrointestinal immune diseases but are associated with significant interindividual variability in efficacy and toxicity, mainly influenced by polymorphisms in Thiopurine S-methyltransferase TPMT and Nudix Hydrolase 15 NUDT15. This study aimed to assess the frequency of TPMT and NUDT15 variants in a pediatric cohort and evaluate their clinical impact to support a pharmacogenetic-guided approach to thiopurine therapy. Methods: Eighty-three pediatric patients with IBD and other autoimmune diseases were genotyped for clinically relevant TPMT and NUDT15 variants using two HRM-PCR assays and were confirmed with sequencing. Variant frequencies were compared to expected population data, and clinical records were reviewed to assess thiopurine dosing, tolerance, and adverse events. Results: Among the cohort, six carried heterozygous TPMT variants *1/*3A, while 2 carried the NUDT15 *1/*9 diplotype, with frequencies higher than expected. Among patients with TPMT variant alleles, some needed dose reductions or treatment discontinuation due to adverse effects, while others tolerated standard dosing without significant issues. Notably, no significant differences in adverse reactions were observed between NUDT15 *1/*9 carriers and wild-type patients. Conclusions: Our results confirm the clinical relevance of TPMT and NUDT15 genotyping to personalize thiopurine therapy in pediatric IBD. Routine implementation of rapid genetic testing, combined with therapeutic drug monitoring and a structured management algorithm, may optimize treatment outcomes and minimize preventable toxicity. Full article

The ongoing challenge of doping in sports has triggered the adoption of advanced scientific strategies for the detection and prevention of doping abuse. This review examines the potential of integrating metabolomics aided by artificial intelligence (AI) and machine learning (ML) for profiling small-molecule metabolites across biological systems to advance anti-doping efforts. While traditional targeted detection methods serve a primarily forensic role—providing legally defensible evidence by directly identifying prohibited substances—metabolomics offers complementary insights by revealing both exogenous compounds and endogenous physiological alterations that may persist beyond direct drug detection windows, rather than serving as an alternative to routine forensic testing. High-throughput platforms such as UHPLC-HRMS and NMR, coupled with targeted and untargeted metabolomic workflows, can provide comprehensive datasets that help discriminate between doped and clean athlete profiles. However, the complexity and dimensionality of these datasets necessitate sophisticated computational tools. ML algorithms, including supervised models like XGBoost and multi-layer perceptrons, and unsupervised methods such as clustering and dimensionality reduction, enable robust pattern recognition, classification, and anomaly detection. These approaches enhance both the sensitivity and specificity of diagnostic screening and optimize resource allocation. Case studies illustrate the value of integrating metabolomics and ML—for example, detecting recombinant human erythropoietin (r-HuEPO) use via indirect blood markers and uncovering testosterone and corticosteroid abuse with extended detection windows. Future progress will rely on interdisciplinary collaboration, open-access data infrastructure, and continuous methodological innovation to fully realize the complementary role of these technologies in supporting fair play and athlete well-being. Full article

Knee osteoarthritis (KOA) is a most prevalent chronic muscoloskeletal disorder causing pain and functional impairment. Accurate predictions of KOA evolution are important for early interventions and preventive treatment planning. In this paper, we propose a novel dynamic hypergraph-based risk model (DyHRM) which integrates the encoder–decoder (ED) architecture with hypergraph convolutional neural networks (HGCNs). The risk model is used to generate longitudinal forecasts of KOA incidence and progression based on the knee evolution at a historical stage. DyHRM comprises two main parts, namely the dynamic hypergraph gated recurrent unit (DyHGRU) and the multi-view HGCN (MHGCN) networks. The ED-based DyHGRU follows the sequence-to-sequence learning approach. The encoder first transforms a knee sequence at the historical stage into a sequence of hidden states in a latent space. The Attention-based Context Transformer (ACT) is designed to identify important temporal trends in the encoder’s state sequence, while the decoder is used to generate sequences of KOA progression, at the prediction stage. MHGCN conducts multi-view spatial HGCN convolutions of the original knee data at each step of the historic stage. The aim is to acquire more comprehensive feature representations of nodes by exploiting different hyperedges (views), including the global shape descriptors of the cartilage volume, the injury history, and the demographic risk factors. In addition to DyHRM, we also propose the HyGraphSMOTE method to confront the inherent class imbalance problem in KOA datasets, between the knee progressors (minority) and non-progressors (majority). Embedded in MHGCN, the HyGraphSMOTE algorithm tackles data balancing in a systematic way, by generating new synthetic node sequences of the minority class via interpolation. Extensive experiments are conducted using the Osteoarthritis Initiative (OAI) cohort to validate the accuracy of longitudinal predictions acquired by DyHRM under different definition criteria of KOA incidence and progression. The basic finding of the experiments is that the larger the historic depth, the higher the accuracy of the obtained forecasts ahead. Comparative results demonstrate the efficacy of DyHRM against other state-of-the-art methods in this field. Full article

Detecting internal defects in narrow and curved pipelines remains a significant challenge, due to the difficulty of achieving reliable defect perception under low-light conditions and generating collision-free motion trajectories. To address these challenges, this article proposes an event-aware ES-YOLO framework, and develops a pipeline defect inspection experimental environment that utilizes a hyper-redundant manipulator (HRM) to insert an event camera into the pipeline in a collision-free manner for defect inspection. First, to address the lack of datasets for event-based pipeline inspection, the ES-YOLO framework is proposed. This framework converts RGB data into an event dataset, N-neudet, which is subsequently used to train and evaluate the detection model. Concurrently, comparative experiments are conducted on steel and acrylic pipelines under three different illumination conditions. The experimental results demonstrate that, under low-light conditions, the event-based detection model significantly outperforms the RGB detection model in defect recognition rates for both types of pipelines. Second, a pipeline defect detection physical system is developed, integrating a visual perception module based on the ES-YOLO framework and a control module for the snake-like HRM. The system controls the HRM using a combination of Nonlinear Model Predictive Control (NMPC) and the Serpentine Crawling Algorithm (SCA), enabling the event camera to perform collision-free inspection within the pipeline. Finally, extensive pipeline insertion experiments are conducted to validate the feasibility and effectiveness of the proposed framework. The results demonstrate that the framework can effectively identify steel pipeline defects in a 2 Lux low-light environment, achieving a detection accuracy of 84%. Full article

This study explores the role of artificial intelligence (AI) in human resource management (HRM), with a focus on recruitment, employee retention, and performance optimization. Through a PRISMA-based systematic literature review, the paper examines many machine learning algorithms including XGBoost, SVM, random forest, and linear regression in decision-making related to employee-attrition prediction and talent management. The findings suggest that these technologies can automate HR processes, reduce bias, and personalize employee experiences. However, the implementation of AI in HRM also presents challenges, including data privacy concerns, algorithmic bias, and organizational resistance. To address these obstacles, the study highlights the importance of adopting ethical AI frameworks, ensuring transparency in decision-making, and developing effective integration strategies. Future research should focus on improving explainability, minimizing algorithmic bias, and promoting fairness in AI-driven HR practices. Full article

A novel algorithm (CP-QRRT*) is proposed for the path planning tasks of hyper-redundant manipulators (HRMs) in confined spaces, addressing the issues of unmet joint angle constraints, redundant planning paths, and long planning times present in previous algorithms. First, the PSO algorithm is introduced to optimize the random sampling process of the RRT series algorithms, enhancing the directionality of the random tree expansion. Subsequently, the method of backtracking ancestor nodes from the Quick-RRT* algorithm is combined to avoid getting trapped in local optima. Finally, a constraint module designed based on the maximum joint angle constraints of the HRM is implemented to limit the path deflection angles. Simulation experiments demonstrate that the proposed algorithm can satisfy the joint angle constraints of the HRM, and the planned paths are shorter and require less time. Full article

Multispectral and hyperspectral image fusion (MS/HS fusion) aims to generate a high-resolution hyperspectral (HRHS) image by fusing a high-resolution multispectral (HRMS) and a low-resolution hyperspectral (LRHS) images. The deep unfolding-based MS/HS fusion method is a representative deep learning paradigm due to its excellent performance and sufficient interpretability. However, existing deep unfolding-based MS/HS fusion methods only rely on a fixed linear degradation model, which focuses on modeling the relationships between HRHS and HRMS, as well as HRHS and LRHS. In this paper, we break free from this observation model framework and propose a new observation model. Firstly, the proposed observation model is built based on the convolutional sparse coding (CSC) technique, and then a proximal gradient algorithm is designed to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as MHF-CSCNet, where the proximal operators are learned using convolutional neural networks. Finally, all trainable parameters can be automatically learned end-to-end from the training pairs. Experimental evaluations conducted on various benchmark datasets demonstrate the superiority of our method both quantitatively and qualitatively compared to other state-of-the-art methods. Full article

This study investigates the factors influencing the aversion of Swiss HRM departments to algorithmic decision-making in the hiring process. Based on a survey provided to 324 private and public HR professionals, it explores how privacy concerns, general attitude toward AI, perceived threat, personal development concerns, and personal well-being concerns, as well as control variables such as gender, age, time with organization, and hierarchical position, influence their algorithmic aversion. Its aim is to understand the algorithmic aversion of HR employees in the private and public sectors. The following article is based on three PLS-SEM structural equation models. Its main findings are that privacy concerns are generally important in explaining aversion to algorithmic decision-making in the hiring process, especially in the private sector. Positive and negative general attitudes toward AI are also very important, especially in the public sector. Perceived threat also has a positive impact on algorithmic aversion among private and public sector respondents. While personal development concerns explain algorithmic aversion in general, they are most important for public actors. Finally, personal well-being concerns explain algorithmic aversion in both the private and public sectors, but more so in the latter, while our control variables were never statistically significant. This said, this article makes a significant contribution to explaining the causes of the aversion of HR departments to recruitment decision-making algorithms. This can enable practitioners to anticipate these various points in order to minimize the reluctance of HR professionals when considering the implementation of this type of tool. Full article