Search Results (87)

The expansion of digital technologies in dairy farming (precision dairy farming) has created new opportunities for the systematic use of data, which can lead to more efficient production processes. This study aimed to develop and evaluate models for predicting daily milk yield from dairy cows during summer. This yield was modeled at the individual level, with days in milk and parity group included as baseline covariates in all analyses. Three feature-set scenarios were defined and evaluated, in which the temperature–humidity index (THI) and milk yield history were added to the baseline variables either separately (Scenarios 1 and 2) or jointly (Scenario 3). Performance was evaluated using walk-forward validation, and feature selection was nested within each iteration’s training window. The performance of the linear mixed model (LMM) was then compared with two machine learning models, random forest (RF) and gradient boosting machine (GBM), within the same experimental framework. In Scenario 3, all three models showed similar fits (R² = 0.92 and concordance correlation coefficient = 0.96), although the GBM model yielded a smaller error (root mean square error [RMSE] = 2.07 ± 0.22, mean absolute error [MAE] = 1.39 ± 0.12) than the RF model (RMSE = 2.10 ± 0.23, MAE = 1.45 ± 0.13) and the LMM (RMSE = 2.15 ± 0.22, MAE = 1.41 ± 0.10). Overall, adding the THI and recent milk yield history to the baseline variables improved short-term prediction accuracy in this dataset, with the GBM model showing the smallest error. These results can support farmers and herd managers in predicting short-term milk yield under heat stress conditions and making timely management decisions. Full article

The neonatal development period from the time of birth can be considered the period of greatest physiological changes throughout the human lifespan. These changes are partly due to dietary or environmental factors and are also modulated by genetic, neuronal, and humoral influences. The focus of research is increasingly on the microbial colonization of the neonatal intestine, since the establishment of a healthy, symbiotic newborn microbiota not only corresponds closely with nutrient metabolism, immune functions, and growth, but also with the brain as part of the so-called “gut–brain axis”. At the same time, a critical time window of opportunity opens up for the early infant microbiota, which is accessible to modulating approaches in favor of normal infant development. Although the definition of “normal” microbiota in infants still remains challenging, the microbiota of infants delivered at term can be discussed as the gold standard—provided they were exclusively breastfed and have not been exposed to antibiotics. Advances in sequencing technologies now also allow us to identify and characterize the microbiota at the strain level and to provide the scientific rationale for new approaches to modulate the early-life microbiome in a more targeted and personalized way—applicable also for formula-fed children who cannot be supplied with human milk. This review addresses the challenges associated with the “healthy” development of a newborn during the first weeks and months of life and discusses potentially modifiable external factors in light of the requirements for the establishment of a functional gut microbiota, gastrointestinal system, and gut–brain axis. Full article

Circulating tumor cells (CTCs) represent a powerful, minimally invasive window into tumor biology and disease evolution. Technological progress over the past decade has markedly improved the ability to isolate, preserve, and interrogate viable CTCs, transforming them from simple prognostic markers to functional tools for precision oncology. Advances in microfluidic platforms, immunomagnetic enrichment, aptamer-based capture, and nanostructured interfaces have expanded the efficiency and fidelity of CTC recovery, enabling comprehensive molecular profiling and ex vivo analysis. These innovations have paved the way for the development of CTC-derived preclinical models, including xenografts, organoids, and chorioallantoic membrane assays, which recapitulate patient-specific tumor heterogeneity and support individualized drug-sensitivity testing. In this review, we summarize current technologies for CTC isolation, outline recent achievements in functional and pharmacological characterization, and discuss the translational impact of CTC-derived models. We further identify persistent challenges and emerging opportunities, highlighting how integration of multi-omics platforms, artificial intelligence, and standardized workflows may accelerate the clinical implementation of CTC-guided personalized therapy. Full article

Norway reached a Battery Electric Vehicle market share of 96% in 2025. The fleet share reached 33%. Other countries are 5–10 years behind Norway. The extraordinary Norwegian development is the result of a 35-year-long complex process involving BEV testing from 1990 and Norwegian BEV industrialization from 1998, supported by a large package of incentives. The incentive package remained in place after the Norwegian actors went bankrupt in 2010 and the global OEMs took over the BEV supply. Norway has a had head start over other countries with high visibility, awareness, and a BEV fleet that accounted for 35% of all BEVs in Europe to build a market from. The incentives made the new OEM BEVs immediately competitive, contrasting with other countries’ insufficient incentives and slow development. A second market expansion followed from 2017 with access to lower-cost and long-range BEVs in more market segments. The EU’s new vehicle CO₂-regulation forced OEMs to sell BEVs on a large scale. BEV technology improved rapidly with longer range and faster charging at a reduced cost, making the incentive even more efficient. The model availability increased rapidly from 2020, while ICEV model availability declined rapidly from 2022, enabling Norway to reach the national target of only selling BEVs from 2025. Norway solved the demand-side challenges of BEV adoption through large market pull incentives. The early supply-side challenges were attempted to be solved with Norwegian BEV production targeting a small-city BEV niche. When that failed, a window of opportunity opened to solve the supply-side challenges with the availability of OEM BEVs. The market scope broadened to commuters and multi-vehicle households and eventually to all new vehicle buyers. By 2020, all demand-side and supply-side challenges were solved, and the transition was accelerated by societal processes. Full article

Against the backdrop of accelerating servitization transformation in the global manufacturing sector, how digital platform capability effectively drives improvements in innovation performance has become a critical issue. Existing research mainly focuses on the instrumental attributes of digital technologies, while relatively few studies examine their strategic role in servitization transformation, particularly the systematic explanation of the “capability–behavior–context–performance” transmission mechanism. To address this gap, this study integrates dynamic capability theory and the opportunity window theory to construct a moderated mediation model that uncovers the internal mechanisms and boundary conditions through which digital platform capability influences servitization innovation performance. Based on survey data from 237 manufacturing firms in Guangdong Province, the empirical results indicate that: (1) digital platform capability and value co-creation both exert significant positive effects on servitization innovation performance; (2) value co-creation mediates the relationship between digital platform capability and servitization innovation performance; and (3) although organizational distance was theoretically expected to function as an important contextual variable, this study does not find evidence supporting its inverted U-shaped moderating effect, suggesting that its role in digital contexts may be more complex. This study not only extends the application of dynamic capability theory and opportunity window theory in servitization innovation settings but also provides managerial insights for manufacturing firms to optimize digital platform strategies and build more resilient and sustainable innovation systems. Full article

The increasing integration of renewable energy sources (RES) in Spain is leading to substantial amounts of surplus electricity, presenting a strategic opportunity for green hydrogen production as a key enabler of energy storage and decarbonisation. This study quantifies this untapped potential for 2024. Based on the difference between installed renewable capacity and actual generation, an economically viable surplus of 18,419 GWh was identified within an optimal 10-h operating window. The hydrogen production potential was modelled for three electrolysis technologies—Alkaline (AEL), Proton Exchange Membrane (PEM) and Anion Exchange Membrane (AEM)—using total energy consumption values of 57.40, 65.55 and 59.95 MWh/t H₂, respectively, including auxiliary systems. The estimated annual hydrogen production ranges from 280,999 t (PEM) to 320,897 t (AEL), with AEM yielding an intermediate value of 307,247 t. The analysis reveals a strong regional concentration, with more than 63% of the potential located in Castile and León, Andalusia, Castile-La Mancha and Extremadura. While this range represents an upper technical limit, it highlights the significant opportunity to valorise surplus renewable energy, contingent on targeted investment and a supportive regulatory framework. Full article

Açaí, a berry emblematic of Amazonian biodiversity, is a major Brazilian product whose market value is largely concentrated in its pulp, leaving the residual biomass—particularly the fibrous seed—underexploited and typically discarded in landfills, with negative environmental and social consequences. To address this gap, this study employs a systematic technology roadmapping approach, integrating bibliometric analysis, patent landscaping, and expert consultations to consolidate fragmented data. This methodology enables the mapping of innovation trajectories across technology readiness levels, product categories, market segments, and key stakeholders. The roadmap identifies emerging trends and opportunity windows for valorizing açaí biomass via integrated biorefinery approaches, moving beyond traditional low-complexity uses such as thermal energy and seed-derived coffee substitutes. The highlighted products include pharmaceutical extracts, cosmetic ingredients, nanopapers, and cellulose nanocrystals, leveraging the biomass’s biochemical composition, notably antioxidants, mannose, and inulin. This methodological framework facilitates a dynamic analysis of technological maturation and market evolution, offering strategic insights to guide industrial investments and policy development. Findings indicate that biorefinery integration enhances resource efficiency and product diversification, situating açaí biomass valorization within broader bioeconomy strategies. The study demonstrates the efficacy of technology roadmapping in structuring prospective innovation pathways and in supporting the sustainable utilization of the Amazonian biomass. Full article

The increasing concern for road safety has driven the development of advanced driver behavior analysis systems. This study presents a comprehensive review of various techniques to detect unsafe driving behaviors, with a particular emphasis on using smartphone sensors. By leveraging data from accelerometers, gyroscopes, and GPS, these methods allow for the detection of aggressive driving patterns, which may result from factors such as driver distraction or drowsiness. Modern sensor technology plays a crucial role in real-time monitoring and has significant potential to enhance vehicle safety systems. A Long Short-Term Memory (LSTM) network combined with a Conv1D layer was trained to analyze driving patterns using a sliding window technique. As technology continues evolving, its application in driver behavior analysis holds great promise for reducing traffic accidents and improving driving habits. Furthermore, the ability to gather and analyze large amounts of data from drivers in various conditions opens new opportunities for more personalized and adaptive safety solutions. This research offers insights into the future direction of driver monitoring systems and the growing impact of mobile and sensor-based solutions in transportation safety. Full article

With the growth of global energy demand, the application of smart grid technology has become widespread. Anomaly detection in power systems is crucial for ensuring the stability and economy of power supply. Deep learning technologies offer new opportunities in this field. This paper proposes a deep learning approach based on Convolutional Autoencoders (CAEs) and Gated Recurrent Units (GRUs) for anomaly detection in smart grid power data. This method integrates three types of feature data, namely user power consumption, line loss correlation, and meter error, and combines the moving window technology to construct a CAE-GRU network model. Experimental results show that, compared with traditional methods, this method has higher accuracy in anomaly detection, which can effectively identify potential problems in the power grid and provide strong support for the optimized operation of the smart grid. Full article

Mediterranean island cities face unique challenges in implementing smart city initiatives due to fragmented governance structures, seasonal economic pressures, and evolving societal expectations. This study investigates how strategic aspirations and public discourse shape the feasibility of smart city policies in insular contexts. Specifically, it combines SOAR (Strengths, Opportunities, Aspirations, Results) analysis with the Overton Window framework to examine both the strategic capacities and normative acceptability of technological interventions. The Overton Window, a model originally developed in political theory, is applied here to evaluate how public and policy acceptance of smart technologies, ranging from digital governance systems to AI-based mobility, varies across different islands. While this study draws on cross-case comparisons of multiple Mediterranean island contexts, the primary data were collected in Athens, Greece, through surveys and focus groups with citizens and stakeholders. The findings reveal disparities in institutional maturity, stakeholder coordination, and levels of citizen support. This study concludes that successful smart city transformation requires both strategic coherence and alignment with evolving public values. It proposes the ‘Ecopolis’ model as a conceptual planning framework that integrates sustainability, citizen participation, and data-driven governance in tourism-dependent island settings. Full article

Virtual reality (VR) devices that integrate eye-tracking and hand-tracking technologies can capture users’ natural eye–hand data in real time within a three-dimensional virtual space, providing new opportunities to explore users’ attentional states during natural 3D interactions. This study aims to develop an attention-state prediction model based on the multimodal fusion of eye and hand features, which distinguishes whether users primarily employ goal-directed attention or stimulus-driven attention during the execution of their intentions. In our experiment, we collected three types of data—eye movements, hand movements, and pupil changes—and instructed participants to complete a virtual button selection task. This setup allowed us to establish a binary ground truth label for attentional state during the execution of selection intentions for model training. To investigate the impact of different time windows on prediction performance, we designed eight time windows ranging from 0 to 4.0 s (in increments of 0.5 s) and compared the performance of eleven algorithms, including logistic regression, support vector machine, naïve Bayes, k-nearest neighbors, decision tree, linear discriminant analysis, random forest, AdaBoost, gradient boosting, XGBoost, and neural networks. The results indicate that, within the 3 s window, the gradient boosting model performed best, achieving a weighted F1-score of 0.8835 and an Accuracy of 0.8860. Furthermore, the analysis of feature importance demonstrated that the multimodal eye–hand features play a critical role in the prediction. Overall, this study introduces an innovative approach that integrates three types of multimodal eye–hand behavioral and physiological data within a virtual reality interaction context. This framework provides both theoretical and methodological support for predicting users’ attentional states within short time windows and contributes practical guidance for the design of attention-adaptive 3D interfaces. In addition, the proposed multimodal eye–hand data fusion framework also demonstrates potential applicability in other three-dimensional interaction domains, such as game experience optimization, rehabilitation training, and driver attention monitoring. Full article

The rapid advancement of terahertz (THz) communication systems has positioned this technology as a key enabler for next-generation telecommunication networks, including 6G, secure communications, and hybrid wireless-optical systems. This review comprehensively analyzes THz communication, emphasizing its integration with free-space optical (FSO) systems to overcome conventional bandwidth limitations. While THz-FSO technology promises ultra-high data rates, it is significantly affected by atmospheric absorption, particularly absorption beyond 500 GHz, where the attenuation exceeds 100 dB/km, which severely limits its transmission range. However, the presence of a lower-loss transmission window at 680 GHz provides an opportunity for optimized THz-FSO communication. This paper explores recent developments in high-power THz sources, such as quantum cascade lasers, photonic mixers, and free-electron lasers, which facilitate the attainment of ultra-high data rates. Additionally, adaptive optics, machine learning-based beam alignment, and low-loss materials are examined as potential solutions to mitigating signal degradation due to atmospheric absorption. The integration of THz-FSO systems with optical and radio frequency (RF) technologies is assessed within the framework of software-defined networking (SDN) and multi-band adaptive communication, enhancing their reliability and range. Furthermore, this review discusses emerging applications such as self-driving systems in 6G networks, ultra-low latency communication, holographic telepresence, and inter-satellite links. Future research directions include the use of artificial intelligence for network optimization, creating energy-efficient system designs, and quantum encryption to obtain secure THz communications. Despite the severe constraints imposed by atmospheric attenuation, the technology’s power efficiency, and the materials that are used, THz-FSO technology is promising for the field of ultra-fast and secure next-generation networks. Addressing these limitations through hybrid optical-THz architectures, AI-driven adaptation, and advanced waveguides will be critical for the full realization of THz-FSO communication in modern telecommunication infrastructures. Full article

The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA). Full article

In recent years, sustainable entrepreneurship has emerged as a dynamic field, driving innovative solutions to environmental, social, and financial issues, as evidenced by the improvement of income systems. The purpose of this study is to explore the impact of the forward-looking strategy on enterprise performance so as to ensure that enterprises can maintain the ability of sustainable development. Foresight can promote the enhancement of sustainable development. Therefore, the current research mainly determines that forward-looking strategies will ultimately affect the performance of enterprises through the impact on their own dynamic capabilities. Through the empirical investigation of 125 enterprises, the corresponding research data are obtained. The results show that the forward-looking strategy has a positive impact on enterprise performance, while enterprise dynamic capability, as an intermediary variable, has a positive impact between the forward-looking strategy and enterprise performance. This research introduces market dynamic capability as a moderating variable to explore whether forward-looking strategies can adapt to changes in the external market environment. Structural equation modeling (SEM) is used to examine the complex relationships between multiple independent and dependent variables of forward-looking strategies and dynamic capabilities, including the impact of latent variables. Under the background of digital economy, digital technology gradually infiltrates the operation of enterprises and plays a vital role in enterprise performance. Digital transformation has become a realistic need for enterprises to respond to changes in the market environment and seek development. The forward-looking strategy has brought new opportunities to solve this problem. The core of the forward-looking strategy lies in “forward thinking” and “pioneering intention”. It not only emphasizes the ability to predict and identify potential opportunity windows in uncertain environments but also pays attention to the cultivation of enterprise resilience and openness to maintaining sustainable competitive advantage. Its foresight can build dynamic capabilities and ensure the sustainable development ability of enterprises through continuous insight into market information and technology resources, such as opportunity recognition perception, knowledge absorption and transformation, resource replacement and innovation, organizational change, and reconstruction. Full article

Background: Pancreatic Ductal Adeno-Carcinoma (PDAC) is a highly aggressive cancer, with limited treatment options. Disruption of the circadian clock, which regulates key cellular processes, has been implicated in PDAC initiation and progression. Hence, targeting circadian clock components may offer new therapeutic opportunities for the disease. This study investigates the cytopathic effects of TH301, a novel CRY2 stabilizer, on PDAC cells, aiming to evaluate its potential as a novel therapeutic agent. Methods: PDAC cell lines (AsPC-1, BxPC-3 and PANC-1) were treated with TH301, and cell viability, cell cycle progression, apoptosis, autophagy, circadian gene, and protein expression profiles were analyzed, using MTT assay, flow cytometry, Western blotting, and RT-qPCR technologies. Results: TH301 proved to significantly decrease cell viability and to induce cell cycle arrest at the G1-phase across all PDAC cell lines herein examined, especially the AsPC-1 and BxPC-3 ones. It caused dose-dependent apoptosis and autophagy, and it synergized with Chloroquine and Oxaliplatin to enhance anti-oncogenicity. The remarkable induction of p21 by TH301 was shown to follow clock- and p53-independent patterns, thereby indicating the critical engagement of alternative mechanisms. Conclusions: TH301 demonstrates significant anti-cancer activities in PDAC cells, thus serving as a promising new therapeutic agent, which can also synergize with approved treatment schemes by targeting pathways beyond circadian clock regulation. Altogether, TH301 likely opens new therapeutic windows for the successful management of pancreatic cancer in clinical practice. Full article