Search Results (112,389)

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact the power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarantee. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with the power and energy balance. Drawing upon the principles of the conditional value at risk theory, the risk indexes of the load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment, and the risk cost of load shedding, a distributed robust optimal scheduling model based on a flexible ramp reserve of thermal power is established. Full article

The targeted therapies and immunotherapies in thoracic oncology, particularly for NS-NSCLC, are associated with an increase in the number of predictive biomarkers to be assessed in routine clinical practice. These treatments are administered thanks to marketing authorization for use in daily practice or are evaluated during clinical trials. Since the molecular targets to be identified are more and more complex and numerous, it is now mandatory to use NGS. NGS can be developed from both tissue and fluid (mainly blood). The blood tests in oncology, so-called “liquid biopsies” (LB), are performed with plasmatic circulating free DNA (cf-DNA) and are complementary to the molecular testing performed with a TB. LB use in lung cancer is associated with international guidelines, but additional algorithms could be set up. However, even if useful for better care of patients, notably with advanced and metastatic NS-NSCLC, until now LB are not often integrated into daily practice, at least in Europe and notably in France. The purpose of this review is to describe the different opportunities and algorithms leading to the identification of the molecular signature of NS-NSCLC, using both tissue and liquid biopsies, and to introduce the principle limitations but also some perspectives in this field. Full article

Foundation Models (FMs) are gaining increasing attention in the biomedical artificial intelligence (AI) ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders—especially those involved in or affected by these clinical and translational applications—are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem. Full article

Many actuating and electro-mechanical devices are driven by DC motors. Gear trains are used to amplify the torque in these motors. They are used in a wide variety of automotives, robotics, and automation applications. However, gears are prone to backlash during their operation of amplifying torques of electromehanical drives. This results in the disengagement of gear teeth when the rotation is reversed. These effects give rise to positional inaccuracies and poor control of the system. This proposed Drive-Anti Drive mechanism is used to track the system’s desired response in the presence of backlash in such cases. The Drive-Anti Drive mechanism consists of two motors rotating in opposite directions. Both the drive and the anti-drive are the DC Machines. The simulation results of the proposed scheme on the tracking control of Inverted Pendulum have been presented. Simulation results depict that the utilization of Drive-Anti Drive system has achieved the target outcome in less than 20 s. However, the target tracking of a system with the utilization of single drives takes 40 s. Setting response of an inverted pendulum is approximately twice as efficient with the utilization of the Drive-Anti Drive mechanism. This approach has been able to effectively track the target in the presence of backlash with the utilization of the Drive-Anti Drive mechanism. Full article

To enhance the service life of shipping equipment and minimize surface wear, this study employs biomimetic principles, integrating fitted structures with jet dynamics to model three configurations: non-smooth structures, single jet structures, and non-smooth jet-coupled structures. We utilized the SST k-ω turbulence model for numerical simulations to investigate the drag reduction characteristics of these structural models. By varying the jet angle and speed, we analyzed the changes in viscous resistance, pressure differential resistance, and drag reduction rates at the wall surface. Furthermore, the mechanisms of compressive stress, velocity fields, vortex structures, and shear stress on drag-reducing surfaces were elucidated, revealing how these factors contribute to drag reduction in non-smooth jet-coupled structures. The results indicate that the non-smooth jet-coupled structure exhibits superior drag reduction performance at a main flow field velocity of 20 m/s. As the jet velocity increases, the viscous drag on the surface of the non-smooth jet-coupled structure decreases, while the pressure differential drag increases. Conversely, variations in the jet angle have a minimal effect on viscous drag but lead to a reduction in pressure differential drag. Specifically, when the jet velocity is set at 1 m/s, and the jet angle is 60°, the drag reduction achieved by the non-smooth jet-coupled structure peaks at 7.48%. Additionally, the non-smooth jet-coupled structure features a larger area characterized by low shear stress, along with an increased boundary layer thickness at the bottom; this configuration effectively reduces surface velocity and consequent viscous drag. Full article

Background: The prognosis of patients diagnosed with locally advanced and metastatic gastric and esophago-gastric junction cancer is critical. The optimal choice of systemic therapy is essential to optimize survival outcomes. Methods: A comprehensive literature review via PubMed and analysis of major oncology congresses (European Society for Medical Oncology and American Society of Clinical Oncology websites) were conducted to ascertain the current status and latest developments in the systemic treatment of patients with localized or advanced gastric and esophago-gastric junction adenocarcinoma. Results: While neoadjuvant and perioperative chemotherapy for localized tumor stages is the preferred approach in the Western Hemisphere, adjuvant chemotherapy remains the preferred course of action in East Asia. The administration of chemotherapy, typically in the form of combinations comprising platinum and fluoropyrimidine compounds in combination with docetaxel, represents a standard of care. Investigations are underway into the potential of immunotherapy and other biologically targeted agents in the perioperative setting. To select the most appropriate therapy for advanced gastric cancer, including adenocarcinoma of the esophago-gastric junction, it is essential to determine biomarkers such as HER2 expression, PD-L1 combined positive score (CPS) (combined positive score), Claudin 18.2, and microsatellite instability (MSI). In the present clinical context, the standard first-line therapy is a combination of fluoropyrimidine and a platinum derivative. The selection of chemotherapy in combination with antibodies is contingent upon the specific biomarker under consideration. Conclusions: This article reviews the current state of the art based on recent clinical trial results and provides an outlook on the future of systemic therapy. Full article

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalanced number between the target and the background spatially and the high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation-learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and spatial–spectral features to improve its representation ability. Firstly, a Graph Convolutional Network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing the detection and background suppression capabilities. The performance of the proposed approach is evaluated using the average scores of $\mathrm{AU}{\mathrm{C}}_{\left(\mathrm{D},\mathrm{F}\right)}$, $\mathrm{AU}{\mathrm{C}}_{\left(\mathrm{F},\tau \right)}$, $\mathrm{AU}{\mathrm{C}}_{\mathrm{BS}}$, and $\mathrm{AU}{\mathrm{C}}_{\mathrm{SNPR}}$. The corresponding values are 0.99660, 0.00078, 0.99587, and 333.629, respectively. These results demonstrate the accuracy of the model in different evaluation metrics, with $\mathrm{AU}{\mathrm{C}}_{\left(\mathrm{D},\mathrm{F}\right)}$ achieving the highest score, indicating strong detection performance across varying thresholds. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture. Full article

The economy is significantly impacted by environmental, social, and governance (ESG) risks. The growth of the economy can be sped up by the effective management of ESG risks through sustainable business practices. To promote sustainable development and to secure the long-term welfare of employees, customers, and all other stakeholders in the economy, companies must adapt and reposition their business strategies and organizational cultures. The goal of this paper is to determine how a set of common ESG elements, chosen from the viewpoints of sustainability and well-being, influence economic growth in the Western Balkan countries. For each ESG component, we used different variables. The information pertains to the five Western Balkan countries of North Macedonia, Albania, Montenegro, Bosnia and Herzegovina, and Serbia. Because of a lack of data, Kosovo is excluded from the study. Then, we compared results from the analysis of the Western Balkan countries with a set of countries in Southeast Europe, which are members of the European Union and essentially coincide with the Western Europe countries. We performed multiple regression analysis with applied fixed effects to the data model. According to the study’s findings, each of the independent variables had no significant impact on the GDP’s annual growth of the Western Balkan countries, but two of the variables, life expectancy at birth and labor force participation, have certain impact on the GDP growth of Southeast Europe countries, which are members of the European Union. The green transition has gained significant importance in the Western Balkan countries as a crucial pathway toward sustainable economic growth, though it introduces a range of new social and economic challenges. Economically, these nations are confronted with considerable funding requirements for development. To build sustainable societies, it would be beneficial for these countries to explore more creative financing strategies. It is advised to establish financing frameworks that not only increase the transparency in policymaking but also ensure greater accountability in their execution. Full article

Background/Objectives: The burden of chronic kidney disease (CKD) is increasing, as is the prevalence of type 2 diabetes mellitus (T2DM). Post-hoc analyses of clinical trials support that sodium–glucose cotransporter-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptors agonists (GLP-1RAs) prevent CKD in T2DM patients. Methods: We used the Spanish primary care database BIFAP to perform a retrospective cohort study with a nested case-control analysis to assess the incidence, risk factors, and the effect of glucose-lowering drugs (GLDs) on the primary prevention of CKD. Results: From a cohort of 515,701 T2DM subjects (2.75 million person-years), we found 89,075 incident CKD cases, yielding an overall incidence rate (95%CI) of 324.3 (322.1–326.5) per 10,000 person-years. In the nested case–control analysis, gout, hyperuricemia, and hyperkalemia were the factors showing the highest AORs. Long-term users (≥3 years) of GLP1-RAs and SGLT-2i, compared to other GLDs, showed a decreased risk for CKD (AOR = 0.85; 95%CI: 0.73–0.99 and AOR = 0.89; 95%CI: 0.74–1.08, respectively), and for incident CKD at KDIGO stages G3-G5 (AOR = 0.72; 95%CI: 0.56–0.94 and AOR = 0.64; 95%CI: 0.46–0.91, respectively). Conclusions: In a real-world primary care setting, the long-term use of GLP-1RAs and SGLT-2i, but not other GLDs, appeared to decrease the risk of incident CKD in T2DM, supporting a role in primary prevention of CKD. Full article

Background/Objectives: Biological products are emerging as therapeutic management options for intervertebral disc (IVD) degenerative affections and lower back pain. Autologous and allogeneic cell therapy protocols have been clinically implemented for IVD repair. Therein, several manufacturing process design considerations were shown to significantly influence clinical outcomes. The primary objective of this study was to preclinically qualify (chondrogenic potential, safety, resistance to hypoxic and inflammatory stimuli) cryopreserved primary progenitor cells (clinical grade FE002-Disc cells) as a potential cell source in IVD repair/regeneration. The secondary objective of this study was to assess the cell source’s delivery potential as cell spheroids (optimization of culture conditions, potential storage solutions). Methods/Results: Safety (soft agar transformation, β-galactosidase, telomerase activity) and functionality-related assays (hypoxic and inflammatory challenge) confirmed that the investigated cellular active substance was highly sustainable in defined cell banking workflows, despite possessing a finite in vitro lifespan. Functionality-related assays confirmed that the retained manufacturing process yielded strong collagen II and glycosaminoglycan (GAG) synthesis in the spheroids in 3-week chondrogenic induction. Then, the impacts of various process parameters (induction medium composition, hypoxic incubation, terminal spheroid lyophilization) were studied to gain insights on their criticality. Finally, an optimal set of technical specifications (use of 10 nM dexamethasone for chondrogenic induction, 2% O₂ incubation of spheroids) was set forth, based on specific fine tuning of finished product critical functional attributes. Conclusions: Generally, this study qualified the considered FE002-Disc progenitor cell source for further preclinical investigation based on safety, quality, and functionality datasets. The novelty and significance of this study resided in the establishment of defined processes for preparing fresh, off-the-freezer, or off-the-shelf IVD spheroids using a preclinically qualified allogeneic human cell source. Overall, this study underscored the importance of using robust product components and optimal manufacturing process variants for maximization of finished cell-based formulation quality attributes. Full article

The quality control (QC) of pharmaceutical-grade cell-therapy products, such as mesenchymal stem cells (MSCs), is challenging. Attempts to develop such products have been hampered by difficulties defining cell-type-specific characteristics and therapeutic mechanisms of action (MoAs). Although we have developed a cell therapy product, FF-31501, consisting of human synovial MSCs (SyMSCs), it was difficult to find specific markers for SyMSCs and to define the cells separately from other MSCs. The purpose of this study was to create a method for identifying and defining SyMSCs from other tissue-derived MSCs and to delve deeper into the mechanism of action of SyMSC-induced meniscus regeneration. Specifically, as a cell-type-dependent approach, we constructed a set of 1143 genes (Amp1200) reported to be associated with MSCs and established a method to evaluate them by correlating gene expression patterns. As a result, it was possible to define SyMSCs separately from other tissue-derived MSCs and non-MSCs. In addition, the gene expression analysis also highlighted TNSF-15. The in vivo rat model of meniscus injury found TNSF-15 to be an essential molecule for meniscus regeneration via SyMSC administration. This molecule and previously reported MoA molecules allowed an MoA-dependent approach to define the mechanism of action for SyMSCs. Therefore, SyMSCs for meniscus regeneration were defined by means of two approaches: the method to separate them from other MSCs and the identification of the MoA molecules. These approaches would be useful for the QC of cell therapy products. Full article

Renal cell carcinoma (RCC) has been increasing in incidence by around 1.5% per year for several years. However, the mortality rate has been decreasing by 1.6% per year, and this can be attributed to stage migration and improvements in treatment. One treatment modality that has emerged in recent years is stereotactic body radiotherapy (SBRT), which is an advanced radiotherapy technique that allows the delivery of high-dose radiation to the tumor while minimizing doses to the organs at risk. SBRT has developed a role in the treatment of early-stage, oligometastatic and oligoprogressive RCC. In localized disease, phase II trials and meta-analyses have shown that SBRT provides a very high probability of long-term local control with a low risk of severe late toxicity. In oligometastatic (OMD) RCC, the same level of evidence has similarly shown good local control and minimal toxicity. SBRT could also delay the necessity to start or switch systemic treatments. Medical societies have started to incorporate SBRT in their guidelines in the treatment of localized disease and OMD. A possible future role of SBRT involves cytoreduction. It is theorized that SBRT can lower tumor burden and enhance immune-related response, but it cannot be recommended until the results of the phase II trials are published. Full article

This study explored the quantitative inversion of the chlorophyll content in Paulownia seedling leaves under drought stress and analyzed the factors influencing the chlorophyll content from multiple perspectives to obtain the optimal model. Paulownia seedlings were selected as the experimental materials for the potted water control experiments. Four drought stress treatments were set up to obtain four types of Paulownia seedlings: one pair of top leaves (T1), two pairs of leaves (T2), three pairs of leaves (T3), and four pairs of leaves (T4). In total, 23 spectral transformations were selected, and the following four methods were adopted to construct the prediction model, select the best spectral preprocessing method, and explore the influence of water bands: partial least squares modeling with all spectral bands (all-band partial least squares, AB-PLS), principal component analysis partial least squares (PCA-PLS), correlation analysis partial least squares (CA-PLS), correlation analysis (water band) partial least squares, ([CA(W)-PLS]), and vegetation index modeling. Based on the prediction accuracy and the uniformity of different leaf positions, the optimal model was systematically explored. The results of the analysis of spectral reflectance showed significant differences at different leaf positions. The sensitive bands of chlorophyll were located near 550 nm, whereas the sensitive bands of water were located near 1440 and 1920 nm. The results of the vegetation index models indicate that the multiple-index models outperformed the single-index models. Accuracy decreased as the number of indicators decreased. We found that different model construction methods matched different optimal spectral preprocessing methods. First derivative spectra (${\mathrm{R}}^{\prime }$) was the best preprocessing method for the AB-PLS, PCA-PLS, and CA-PLS models, whereas the inverse log spectra ($\mathrm{l}\mathrm{o}\mathrm{g}(1/\mathrm{R})$) was the best preprocessing method for the CA(W)-PLS model. Among the 14 indices, the green normalized difference vegetation index (GNDVI) was most correlated with the chlorophyll content sensitivity indices, and the water index (WI) was most correlated with the water sensitive indices. At the same time, the water band affected the cross validation accuracy. When characteristic bands were used for modeling, the cross validation accuracy was significantly increased. In contrast, when vegetation indices were used for modeling, the accuracy of the cross validation increased slightly but its predictive ability was reduced; thus, these changes could be ignored. We found that leaf position also affected the prediction accuracy, with the first pair of top leaves exhibiting the worst predictive ability. This was a bottleneck that limited predictive capability. Finally, we found that the CA(W)-PLS model was optimal. The model was based on 23 spectral transformations, four PLS construction methods, water bands, and different leaf positions to ensure systematicity, stability, and applicability. Full article

White identity is forged through violence. The moment that whites aspired to be white, they set themselves up for immediate, inevitable failure: they try to inhabit an identity of superiority that cannot exist, and this sets them into a perpetual identity crisis, an existential emergency that threatens who they are and who they think they want to be. The ensuing identity formation—white by force—comprises an entire set of strategies, tactics, institutions, and structures meant to prop up an inherently failure-based identity and to do so through brutality, resentment, anger, contrived fear, and murder. Such an identity impacts everyone, including white people themselves, in devastating ways. In what following article, I will put forward a theoretical model called “White by Force and the Racialized State of Exception”, conceptualizing key aspects of this model to provide an emerging vocabulary for studying, discussing, and dismantling white identity, violence, authoritarianism, racism, and the existential crises that we are all facing. Our discourse around race and racism, since the Civil Rights Era, needs new language from which to diagnose an inherently violent identity formation that ultimately benefits no one, especially not people of color or white people from lower socioeconomic strata. Full article

Robotic systems are crucial in modern manufacturing. Complex assembly tasks require the collaboration of multiple robots. Their orchestration is challenging due to tight tolerances and precision requirements. In this work, we set up two Franka Panda robots performing a peg-in-hole insertion task of 1 mm clearance. We structure the control system hierarchically, planning the robots’ feedback-based trajectories with a central policy trained with reinforcement learning. These trajectories are executed by a low-level impedance controller on each robot. To enhance training convergence, we use reverse curriculum learning, novel for such a two-armed control task, iteratively structured with a minimum requirements and fine-tuning phase. We incorporate domain randomization, varying initial joint configurations of the task for generalization of the applicability. After training, we test the system in a simulation, discovering the impact of curriculum parameters on the emerging process time and its variance. Finally, we transfer the trained model to the real-world, resulting in a small decrease in task duration. Comparing our approach to classical path planning and control shows a decrease in process time, but higher robustness towards calibration errors. Full article