Search Results (65)

Artificial intelligence has taken a seat at the executive table and is threatening the fact that human beings are the only ones who should be in a position of power. This article gives conjectures on the future of leadership in which managers will collaborate with learning algorithms in the Neural Adaptive Artificial Intelligence Leadership Model, which is informed by the transformational literature on leadership and socio-technical systems, as well as the literature on algorithmic governance. We assessed the model with thirty in-depth interviews, system-level traces of behavior, and a verified survey, and we explored six hypotheses that relate to algorithmic delegation and ethical oversight, as well as human judgment versus machine insight in terms of agility and performance. We discovered that decisions are made quicker, change is more effective, and interaction is more vivid where agile practices and good digital understanding exist, and statistical tests propose that human flexibility and definite governance augment those benefits as well. It is single-industry research that contains self-reported measures, which causes research to be limited to other industries that contain more objective measures. Practitioners are provided with a practical playbook on how to make algorithmic jobs meaningful, introduce moral fail-safes, and build learning feedback to ensure people and machines are kept in line. Socially, the practice is capable of minimizing bias and establishing inclusion by visualizing accountability in the code and practice. Filling the gap between the theory of leadership and the reality of algorithms, the study provides a model of intelligent systems leading in organizations that can be reproduced. Full article

Eccentric muscle actions are integral to human movement, rehabilitation, and performance training due to their characteristic high force output (overload) and low energy cost and perceived exertion. Despite the growing use of eccentric devices, a gap in the research exists exploring multi-muscle activation profiles during multi-joint eccentric-only, isokinetic exercise. This study aimed to quantify and compare surface electromyographic (EMG) activity of four leg muscles—vastus lateralis (VL), tibialis anterior (TA), biceps femoris (BF), and medial gastrocnemius (GM)—during a standardized (isokinetic) submaximal eccentric multi-joint exercise using the Eccentron dynamometer. Eighteen healthy adults performed eccentric exercise at 40% of their maximal eccentric strength. Surface EMG data were analyzed using root mean square (RMS) and integrated EMG (iEMG) variables. Repeated-measures ANOVAs and effect sizes (ES) were used to evaluate within-subject differences across muscles. Results showed significantly greater activation in the VL compared to all other muscles (p < 0.05; and ES of 1.28–3.17 versus all other muscles), with the TA also demonstrating higher activation than the BF (p < 0.05). The BF exhibited the lowest activation, suggesting limited hamstring engagement. These findings highlight the effectiveness of the multi-joint isokinetic eccentric leg press movement (via an Eccentron machine) in targeting the quadriceps and dorsiflexors, while indicating the possible need for supplementary hamstring and plantar flexor exercises when aiming for a comprehensive lower body training routine. This study provides important insights for optimizing eccentric training protocols and rehabilitation strategies. Full article

Existing frameworks for classifying the fidelity for object detection tasks do not consider false positive likelihood and object uniqueness. Inspired by the Detection, Recognition, Identification (DRI) framework proposed by Johnson 1958, we propose a new modified framework that defines three categories as visible anomaly, identifiable anomaly, and unique identifiable anomaly (AIU) as determined by human interpretation of imagery or geophysical data. These categories are designed to better capture false positive rates and emphasize the importance of identifying unique versus non-unique targets compared to the DRI Index. We then analyze visual, thermal, and multispectral UAV imagery collected over a seeded minefield and apply the AIU Index for the landmine detection use-case. We find that RGB imagery provided the most value per pixel, achieving a 100% identifiable anomaly rate at 125 pixels on target, and the highest unique target classification compared to thermal and multispectral imaging for the detection and identification of surface landmines and UXO. We also investigate how the AIU Index can be applied to machine learning for the selection of training data and informing the required action to take after object detection bounding boxes are predicted. Overall, the anomaly, identifiable anomaly, and unique identifiable anomaly index prescribes essential context for false-positive-sensitive or resolution-poor object detection tasks with applications in modality comparison, machine learning, and remote sensing data acquisition. Full article

Objectives: This study aimed to evaluate the effects of different titanium (Ti) anodized surfaces on soft tissue healing around dental implant abutments. Methods: Discs of machined (MC), pink anodized (PA) and yellow anodized (YA) surfaces were morphologically characterized and evaluated in vitro. Cell adhesion and collagen synthesis by human gingival fibroblasts (hGFs) were assessed to evaluate the regenerative potential of the surfaces under study. Their inflammatory potential was evaluated in THP-1 cell cultures by measuring cytokine secretion, and their proteomic adsorption patterns were characterized using nano-liquid chromatography mass spectrometry (nLC-MS/MS). Statistical significance was considered at 5%. In relation to proteomics, statistical differences were evaluated using the Student t-test with the Perseus application. Results: The anodization process resulted in a reduction in the surface roughness parameter (Ra) relative to the machined titanium (p < 0.05). No differences in hGF adhesion were found between the surfaces after one day. PA induced increased hGF collagen synthesis after 7 days (p < 0.05). The secretion of TNF-α was lower for anodized surfaces than for MC, and its concentration was lower for PA than for YA (p < 0.05). In turn, TGF-β was higher for PA and YA versus MC after one and three days of culture. A total of 176 distinct proteins were identified and 26 showed differences in adhesion between the anodized surfaces and MC. These differential proteins were related to coagulation, lipid metabolism, transport activity, plasminogen activation and a reduction in the immune response. Conclusions: Anodized Ti surfaces showed promising anti-inflammatory and regenerative potential for use in dental implant abutments. Anodization reduced surface roughness, increased collagen synthesis and lowered TNF-α secretion while increasing TGF-β levels compared to machined surfaces. Identified proteins related to coagulation and lipid metabolism supported these findings. Clinical relevance: Anodized surfaces could offer improved short-term peri-implant soft tissue healing over machined surfaces. The analysis of abutment surface, instead of implant surface, is a new approach that can provide valuable information. Full article

Background and Objectives: Rectal cancer management increasingly relies on watch-and-wait strategies after neoadjuvant chemoradiotherapy (nCRT). Accurate, non-invasive prediction of pathological complete response (pCR) remains elusive. Emerging evidence suggests that gut-microbiome composition modulates radio-chemosensitivity. We systematically reviewed primary studies that correlated baseline or on-treatment gut-microbiome features with nCRT response in locally advanced rectal cancer (LARC). Methods: MEDLINE, Embase and PubMed were searched from inception to 30 April 2025. Eligibility required (i) prospective or retrospective human studies of LARC, (ii) faecal or mucosal microbiome profiling by 16S, metagenomics, or metatranscriptomics, and (iii) response assessment using tumour-regression grade or pCR. Narrative synthesis and random-effects proportion meta-analysis were performed where data were homogeneous. Results: Twelve studies (n = 1354 unique patients, median sample = 73, range 22–735) met inclusion. Four independent machine-learning models achieved an Area Under the Receiver Operating Characteristic curve AUROC ≥ 0.85 for pCR prediction. Consistently enriched taxa in responders included Lachnospiraceae bacterium, Blautia wexlerae, Roseburia spp., and Intestinimonas butyriciproducens. Non-responders showed over-representation of Fusobacterium nucleatum, Bacteroides fragilis, and Prevotella spp. Two studies linked butyrate-producing modules to radiosensitivity, whereas nucleotide-biosynthesis pathways conferred resistance. Pooled pCR rate in patients with a “butyrate-rich” baseline profile was 44% (95% CI 35–54) versus 21% (95% CI 15–29) in controls (I² = 18%). Conclusions: Despite heterogeneity, convergent functional and taxonomic signals underpin a microbiome-based radiosensitivity axis in LARC. Multi-centre validation cohorts and intervention trials manipulating these taxa, such as prebiotics or live-biotherapeutics, are warranted before clinical deployment. Full article

Objective: This in vitro study aimed to evaluate the influence of cervical margin design—tangential versus chamfer—on the fracture resistance of monolithic crowns fabricated from lithium disilicate and zirconia ceramics. Materials and Methods: Forty extracted human molars were randomly assigned to two preparation types: chamfer and tangential. Each group was restored with CAD/CAM-fabricated crowns made from either zirconia (IPS e.max^® ZirCAD Prime) or lithium disilicate (IPS e.max^® CAD), resulting in four subgroups (n = 10). Standardized adhesive cementation protocols were applied. After 24 h storage in distilled water, the specimens underwent static load-to-failure testing using a ZwickRoell ProLine Z005 universal testing machine. Results: Zirconia crowns with chamfer margins exhibited the highest mean fracture resistance (2658 N), while lithium disilicate crowns with tangential margins showed the lowest (1862 N). Chamfer preparation significantly increased the fracture resistance of lithium disilicate crowns (p < 0.01), whereas margin design had no significant effect on zirconia. All restorations exceeded physiological masticatory forces, confirming their clinical viability. Conclusions: Cervical margin design significantly affected the fracture performance of lithium disilicate crowns but not zirconia. Chamfer preparations are recommended when using lithium disilicate to optimize mechanical strength. These findings underscore the importance of preparation geometry in guiding material selection for CAD/CAM ceramic restorations. Full article

Plant breeding is considered to be the science and art of genetically improving plants according to human needs. Breeders in this context oftentimes face the difficult task of selecting among thousands of genotypes for dozens of traits simultaneously. Using a breeder’s selection decisions from a commercial wheat breeding program as a case study, this study investigated the possibility of implementing a recommender system based on the breeder’s preferences to support early-generation selection decisions in plant breeding. The target trait was the retrospective binary classification of selected versus non-selected breeding lines during a period of five years, while the selection decisions of the breeder were predicted by various machine learning models. The explained variance of these selection decisions was of moderate magnitude (${\mathsf{\rho }}_{\mathrm{S}\mathrm{N}\mathrm{P}}^2$ = 0.45), and the models’ precision suggested that the breeder’s selection decisions were to some extent predictable (~20%), especially when some of the pending selection candidates were part of the training population (~30%). Training machine learning algorithms with breeders’ selection decisions can thus aid breeders in their decision-making processes, particularly when integrating human and artificial intelligence in the form a recommender system to potentially reduce a breeder’s effort and the required time to find interesting selection candidates. Full article

Many tasks in a modern household are performed by machines, e.g., a dishwasher or a vacuum cleaner, and in the near future most household tasks will be performed by smart service robots. This will relieve the residents, who in turn can enjoy their free time. This newly gained free time will turn out to cause the so-called spare time rebound effect due to more resource consumption. We roughly quantify this rebound effect and propose a CO₂-budget model to reduce or even avoid it. In modern industry, automation and AI are taking over work from humans, leading to higher productivity of the company as a whole. This is the main reason for economic growth, which leads to environmental problems due to higher consumption of natural resources. We show that, even though the effects of automation at home and in the industry are different (free time versus higher productivity), in the end they both lead to more resource consumption and environmental pollution. We discuss possible solutions to this problem, such as carbon taxes, emissions trading systems, and a carbon budget. Full article

Human hands have the unique ability to classify material properties, such as hardness, using mechanoreceptors and tactile information. Previous studies have demonstrated hardness classification using Commercial Off-The-Shelf (COTS) sensors but lacked robotic integration considerations. This study explores the integration of multiple COTS sensors, inspired by mechanoreceptors, for classifying material hardness. The sensors were used to classify objects into three categories—hard, soft, and flexible—based on the qualitative Shore hardness scale. The aim was to identify the optimal sensor topology configuration that delivers high accuracy, using machine learning algorithms provided in the literature. The results suggest that the Random Forest Classifier is the most suitable algorithm, showcasing accuracies ranging from 90% to 98.7%, across various sensor topologies. The ‘PFV’ topology, comprising a potentiometer (P), force sensor (F), and vibration sensor (V), achieved the highest accuracy of 98.7%, while the ‘FPV’ and ‘FVP’ recorded accuracies between 96% and 97.5%. The topology of FPV and FVP have the most closely related configuration to that of mechanoreceptors; however, the results show that PFV outperforms this configuration. While the PFV topology marginally outperforms the mechanoreceptor-inspired configurations, the results demonstrate that bio-inspired sensor arrangements provide a robust solution for hardness classification in robotics. The PFV topology performs better than FPV in terms of prediction speed, with an average prediction time of 8.31 ms (millisecond) for PFV versus 13.93 ms for FPV. PFV and FPV achieved 12 and 13 correct predictions, respectively, out of 18 objects. The faster prediction times of PFV make it particularly advantageous for applications requiring quick and accurate decision-making for robotic applications. Full article

Code obfuscation has become an essential practice in modern software development, designed to make source or machine code challenging for both humans and computers to comprehend. It plays a crucial role in cybersecurity by protecting intellectual property, safeguarding trade secrets, and preventing unauthorized access or reverse engineering. However, the lack of transparency in obfuscated code raises significant ethical concerns, including the potential for harmful or unethical uses such as hidden data collection, malicious features, back doors, and concealed vulnerabilities. These issues highlight the need for a balanced approach that ensures the protection of developers’ intellectual property while addressing ethical responsibilities related to user privacy, transparency, and societal impact. This paper investigates various code obfuscation techniques, their benefits, challenges, and practical applications, underscoring their relevance in contemporary software development. This study examines obfuscation methods and tools, evaluates machine learning models—including Random Forest, Gradient Boosting, and Support Vector Machine—and presents experimental results aimed at classifying obfuscated versus non-obfuscated files. Our findings demonstrate that these models achieve high accuracy in identifying obfuscation methods employed by tools such as Jlaive, Oxyry, PyObfuscate, Pyarmor, and py-obfuscator. This research also addresses emerging ethical concerns and proposes guidelines for a balanced, responsible approach to code obfuscation. Full article

In occupational domains such as sports, healthcare, driving, and military, both individuals and small groups are expected to perform challenging tasks under adverse conditions that induce transient cognitive states such as stress, workload, and uncertainty. Wearable and standoff 6DOF sensing technologies are advancing rapidly, including increasingly miniaturized yet robust inertial measurement units (IMUs) and portable marker-less infrared optical motion tracking. These sensing technologies may offer opportunities to track overt physical behavior and classify cognitive states relevant to human performance in diverse human–machine domains. We describe progress in research attempting to distinguish cognitive states by tracking movement behavior in both individuals and small groups, examining potential applications in sports, healthcare, driving, and the military. In the context of military training and operations, there are no generally accepted methods for classifying transient mental states such as uncertainty from movement-related data, despite its importance for shaping decision-making and behavior. To fill this gap, an example data set is presented including optical motion capture of rifle trajectories during a dynamic marksmanship task that elicits variable uncertainty; using machine learning, we demonstrate that features of weapon trajectories capturing the complexity of motion are valuable for classifying low versus high uncertainty states. We argue that leveraging metrics of human movement behavior reveals opportunities to complement relatively costly and less portable neurophysiological sensing technologies and enables domain-specific human–machine interfaces to support a wide range of cognitive functions. Full article

Gesture recognition has become a significant part of human–machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language. Full article

Companies are increasingly using artificial intelligence (AI) to provide users with product recommendations, but its efficacy is inconsistent. Drawing upon social exchange theory, we examine the effects of product recommenders and their levels of self-disclosure on transaction costs. Specifically, we recruited 78 participants and conducted a 2 × 2 online experiment in which we manipulated product recommenders (human versus AI) and examined how self-disclosure levels (high versus low) affect consumers’ return intentions. We predicted and found that a low level of self-disclosure from human recommenders instead of AI counterparts results in higher emotional support, which leads to lower transaction costs. However, under high levels of self-disclosure, consumers’ emotional support and subsequent transaction costs do not differ between human and AI recommenders. Accordingly, we provide theoretical insights into the roles of self-disclosure and emotional support in human–machine interactions, and we contribute to sustainable AI practices by enhancing the efficiency of business operations and advancing broader sustainability objectives. Full article

In the digital era, the (r)evolution of neural machine translation (NMT) has reshaped both the market and translators’ workflow. However, the adoption of this technology has not fully reached the creative field of literary translation. Against this background, this study aims to explore to what extent NMT systems can be used to translate the creative challenges posed by idioms, specifically manipulated multiword expressions (MWEs) found in literary texts. To carry out this pilot study, five manipulated MWEs were selected from a fantasy novel and machine-translated (English > Spanish) by four NMT systems (DeepL, Google Translate, Bing Translator, and Reverso). Then, each NMT output as well as a human translation are assessed by six professional literary translators by using a human evaluation sheet. Based on these results, the creativity obtained in each translation method was calculated. Despite the satisfactory performance of both DeepL and Google Translate, HT creativity was highly superior in almost all manipulated MWEs. To the best of our knowledge, this paper not only contributes to the ongoing study of NMT applied to literature, but it is also one of the few studies that delve into the almost unexplored field of assessing creativity in neural machine-translated MWEs. Full article

Lipids are emerging as important potential targets for the early diagnosis and prognosis of several inflammatory diseases. Studying the lipid profiles is important for understanding cellular events such as low-grade inflammation, a condition common to many human diseases, including cancer, neurodegenerative diseases, diabetes, and obesity. This work aimed to explore lipid signatures in an inflammation cellular model using an advanced bioanalytical approach complemented by Machine Learning techniques. Analyses based on the high-resolution mass spectrometry of extracted lipids in TNF-α inflamed cells (R3/1 NF-κB reporter cells) versus lipids in control cells resulted in 469 quantified lipids, of which 20% were phosphatidylcholines (PCs) and phosphatidylethanolamines (PEs), 10% were sphingomyelins (SMs), 6% were phosphatidylinositols (PIs), 7% were ceramides (Cer), 6% were phosphatidylglycerols (PGs), and 5% were phosphatidylserines (PSs). TNF-α induced a significant alteration compared to the control, with a fold change higher than 1.5; of the 88 lipids, 71 were upregulated and 17 were downregulated, impacting various pathways as revealed by network analyses. To validate the inflammation model, the TNF-α induced cells were treated with polyphenols from thinned young apples (TAPs), which are known to have anti-inflammatory properties. The dysregulation of ceramides (Cer(d18:1/23:0), Cer(d18:1/23:0), and Cer(d18:1/22:0)) observed in TNF-α inflamed cells was completely reverted after TAP treatment. Network analyses showed the alteration of arachidonic acid and TNF signaling, which were modulated by polyphenols from thinned young apples. The results highlighted the potentiality of the inflammatory model and the bioanalytical approach to describe lipid profiles in complex biological matrices and different states. In addition, the quantified lipids were interpreted by an Artificial Intelligence approach to identify relevant signatures and clusters of lipids that can impact cellular states. Lastly, this study underlines both the potential applications of lipidomics combined with Machine Learning and how to build and validate Machine Learning models to predict inflammation based on lipid-related pattern signatures. Full article