Palm kernel cake (PKC), a byproduct of palm oil extraction, serves an important role in Ecuador’s animal feed industry. The emergence of yellow-orange fungal growth in PKC on some cattle farms in Ecuador sparked concerns within the cattle industry regarding a potential mycotoxin-producing fungus on this substrate. Due to the limited availability of analytical chemistry techniques in Ecuador for mycotoxin detection, we chose to isolate and identify the fungus to determine its association with mycotoxin-producing genera. Through molecular identification via ITS region sequencing, we identified the yellow-orange fungus as the yeast Candida ethanolica. Furthermore, we isolated two other fungi—the yeast Pichia kudriavzevii, and the fungus Geotrichum candidum. Molecular identification confirmed that all three species are not classified as mycotoxin-producing fungi but in contrast, the literature indicates that all three have demonstrated antifungal activity against Aspergillus and Penicillium species, genera associated with mycotoxin production. This suggests their potential use in biocontrol to counter the colonization of harmful fungi. We discuss preventive measures against the fungal invasion of PKC and emphasize the importance of promptly identifying fungi on this substrate. Rapid recognition of mycotoxin-producing and pathogenic genera holds the promise of mitigating cattle intoxication and the dissemination of mycotoxins throughout the food chain. Full article

Vesicoureteral reflux (VUR) is defined as the urine backflow from the urinary bladder to the pyelo-caliceal system. In contrast, intrarenal reflux (IRR) is the backflow of urine from the renal calyces into the tubulointerstitial space. VURs, particularly those associated with IRR can result in reflux nephropathy when accompanied by urinary tract infection (UTI). The prevalence of IRR in patients with diagnosed VUR is 1–11% when using voiding cystourethrography (VCUG), while 11.9–61% when applying the contrast-enhanced voiding urosonography (ceVUS). The presence of IRR diagnosed by VCUG often correlates with parenchymal scars, when diagnosed by a 99mTc dimercaptosuccinic acid scan (DMSA scan), mostly in kidneys with high-grade VURs, and when diagnosed by ceVUS, it correlates with the wide spectrum of parenchymal changes on DMSA scan. The study performed by both ceVUS and DMSA scans showed IRRs associated with non-dilated VURs in 21% of all detected VURs. A significant difference regarding the existence of parenchymal damage was disclosed between the IRR-associated and IRR-non-associated VURs. A higher portion of parenchymal changes existed in the IRR-associated VURs, regardless of the VUR grade. That means that kidneys with IRR-associated VURs represent the high-risk group of VURs, which must be considered in the future classification of VURs. When using ceVUS, 62% of places where IRR was found were still unaffected by parenchymal changes. That was the basis for our recommendation of preventive use of long-term antibiotic prophylaxis until the IRR disappearance, regardless of the VUR grade. We propose a new classification of VURs using the ceVUS method, in which each VUR grade is subdivided based on the presence of an IRR. Full article

Artificial intelligence emerges as a powerful catalyst poised to reshape the global sustainability landscape by facilitating the attainment of Sustainable Development Goals (SDGs). This comprehensive Delphi study meticulously probes the insights of domain experts, shedding light on the strategic utilization of AI to advance these critical sustainability objectives. Employing rigorous statistical techniques, encompassing measures of central tendency and interquartile deviation, this research scrutinizes consensus dynamics among experts and elucidates potential variations in their viewpoints. The findings resoundingly convey experts’ collective positive perspective regarding AI’s pivotal role in propelling the SDGs forward. Through two iterative rounds of extensive discussions, a compelling consensus crystallizes—AI indeed exerts an overall positive impact, exemplified by a robust mean score of 78.8%. Intriguingly, distinct SDGs manifest varied propensities toward AI intervention, with Goals 6, 7, 8, 9, 11, 13, 14, and 15 basking in the radiance of highly positive impacts. Goals 1, 2, 3, 4, 5, 10, and 12 exhibit positive impact scores, indicating a juncture ripe for positive advancements. Meanwhile, Goal 16 and Goal 17 languish with neutral scores, signifying a juncture demanding nuanced deliberations about AI’s impact on peace, justice, and strong institutions as well as on partnerships for the goals, respectively. This paper underscores AI as a formidable instrument poised to address humanity’s most pressing challenges while harmonizing seamlessly with the overarching SDG objectives. It gracefully dovetails into established practices across pivotal domains such as health, education, and resilient infrastructures, amplifying the collective global endeavor to navigate the path toward a more sustainable future. Full article

This manuscript details a comprehensive investigation into the synthesis, structural characterization, thermal stability, and optical properties of nickel-containing hybrid perovskites, namely CH₃NH₃NiCl₃, CsNiCl₃, and CH₃NH₃NiBrCl₂. The focal point of this study is to unravel the intricate crystal structures, thermal behaviors, and optical characteristics of these materials, thereby elucidating their potential application in energy conversion and storage technologies. X-ray powder diffraction measurements confirm that CH₃NH₃NiCl₃ adopts a crystal structure within the Cmcm space group, while CsNiCl₃ is organized in the P6₃/mmc space group, as reported previously. Such structural diversity underscores the complex nature of these perovskites and their potential for tailored applications. Thermal analysis further reveals the stability of CH₃NH₃NiCl₃ and CH₃NH₃NiBrCl₂, which begin to decompose at 260 °C and 295 °C, respectively. The optical absorption properties of these perovskites studied by UV-VIS-NIR spectroscopy revealed the bands characteristic of Ni²⁺ ions in an octahedral environment. Notably, these absorption bands exhibit subtle shifts upon bromide substitution, suggesting that optical properties can be finely tuned through halide modification. Such tunability is paramount for the design and development of materials with specific optical requirements. By offering a detailed examination of these properties, the study lays the groundwork for future advancements in material science, particularly in the development of innovative materials for sustainable energy technologies. Full article

Underwater acoustic communication is fraught with challenges, including signal distortion, noise, and interferences unique to aquatic environments. This study aimed to advance the field by developing a novel underwater modem system that utilizes machine learning for signal classification, enhancing the reliability and clarity of underwater transmissions. This research introduced a system architecture incorporating a Lattice Semiconductors FPGA for signal modulation and a half-pipe waveguide to emulate the underwater environment. For signal classification, support vector machines (SVMs) were leveraged with the continuous wavelet transform (CWT) employed for feature extraction from acoustic signals. Comparative analysis with traditional signal processing techniques highlighted the efficacy of the CWT in this context. The experiments and tests carried out with the system demonstrated superior performance in classifying modulated signals under simulated underwater conditions, with the SVM providing a robust classification despite the presence of noise. The use of the CWT for feature extraction significantly enhanced the model’s accuracy, eliminating the need for further dimensionality reduction. Therefore, the integration of machine learning with advanced signal processing techniques presents a promising research line for overcoming the complexities of underwater acoustic communication. The findings underscore the potential of data mining methodologies to improve signal clarity and transmission reliability in aquatic environments. Full article

Small-scale agriculture is important. However, there are still limitations regarding the implementation of technologies in small-scale agriculture due to the high costs accompanying them. Therefore, it is essential to seek viable and low-cost solutions since the insertion of technologies in agriculture, especially irrigated agriculture, guarantees the sustainable expansion of production capacity. The present work applied the Internet of Things concept to an automated irrigation system powered by photovoltaic panels. The materials used in the prototype consisted of Arduino Uno R3, the ESP8266 development board, a soil moisture sensor, a current sensor, a voltage sensor, a flow sensor, and a humidity and temperature sensor. The prototype was designed to take system readings and send them to the Adafruit platform IO. Furthermore, it was programmed to perform remote irrigation control, enabling this to be activated from distant points through the platform. The medium proved efficient for the monitoring and remote control of the system. This indicates that it is possible to use this medium in small automated irrigation systems. Full article

The time–frequency-domain reflection method (TFDR) based on the Wigner–Ville distribution (WVD) is confronted with the problem of cross-term interference in existing methods to locate power cable defects. Therefore, a new method of locating cable defects based on Gaussian narrow-band envelope signals and the S-transform is proposed in this paper. In this method, the wide-band cable transfer function is obtained by adjusting the parameters of the Gaussian narrow-band envelope signal because the Gaussian narrow-band envelope signal has a good frequency-adjusting ability and time–frequency characteristics. Then, the time–frequency of the cable signal is transformed by the generalized S-transform, and the time delay of the modular matrix of the transformation matrix is estimated by the generalized cross-correlation algorithm to complete the accurate detection of the cable defect’s location. Compared with traditional methods, the proposed method can adaptively adjust the analysis time width according to the frequency change and provide intuitive time–frequency characteristics without cross-term interference. Finally, the effectiveness and practicability of the proposed method are verified in MATLAB 2017_a by simulating a 40 m/10 kV medium-voltage power cable and submarine cable with a length of 32 km. Full article

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes. Full article

Matrix multiplication plays a crucial role in various engineering and scientific applications. Cannon’s algorithm, executed within two-dimensional systolic arrays, significantly enhances computational efficiency through parallel processing. However, as the matrix size increases, reliability issues become more prominent. Although the previous work has proposed a fault-tolerant mechanism, it is only suitable for scenarios with a limited number of faulty processing elements (PEs). This paper introduces a pair-matching mechanism, assigning a fault-free PE as a proxy for each faulty PE to execute its tasks. Our fault-tolerant mechanism comprises two stages: in the first stage, each fault-free PE completes its designated computations; in the second stage, computations intended for each faulty PE are executed by its assigned fault-free PE proxy. The experimental results demonstrate that compared to the previous work, our approach not only significantly improves the fault tolerance of systolic arrays (applicable to scenarios with a higher number of faulty PEs) but also reduces circuit areas. Therefore, the proposed approach proves effective in practical applications. Full article

Manganese oxide-cerium oxide supported on titanate nanotubes (i.e., MnCe/TiNTs) were prepared and their catalytic activities towards NH₃-SCR of NO were tested. The results indicated that the MnCe/TiNT catalyst can achieve a high NO removal efficiency above 95% within the temperature range of 150–350 °C. Even after exposure to a HCl-containing atmosphere for 2 h, the NO removal efficiency of the MnCe/TiNT catalyst maintains at approximately 90% at 150 °C. This is attributed to the large specific surface area as well as the unique hollow tubular structure of TiNTs that exposes more Ce atoms, which preferentially react with HCl and thus protect the active Mn atoms. Moreover, the abundant OH groups on TiNTs serve as Brønsted acid sites and provide H protons to expel Cl atom from the catalyst surface. The irreversible deactivation caused by HCl can be alleviated by H₂O. That is because the dissociated adsorption of H₂O on TiNTs forms additional OH groups and relieves HCl poisoning. Full article

Managing cellular networks in the Beyond 5G (B5G) era is a complex and challenging task requiring advanced deep learning approaches. Traditional models focusing on internet traffic (INT) analysis often fail to capture the rich temporal and spatial contexts essential for accurate INT predictions. Furthermore, these models do not account for the influence of external factors such as weather, news, and social trends. This study proposes a multi-source CNN-RNN (MSCR) model that leverages a rich dataset, including periodic, weather, news, and social data to address these limitations. This model enables the capture and fusion of diverse data sources for improved INT prediction accuracy. An advanced deep learning model, the transformer-enhanced CNN-RNN (TE-CNN-RNN), has been introduced. This model is specifically designed to predict INT data only. This model demonstrates the effectiveness of transformers in extracting detailed temporal-spatial features, outperforming conventional CNN-RNN models. The experimental results demonstrate that the proposed MSCR and TE-CNN-RNN models outperform existing state-of-the-art models for traffic forecasting. These findings underscore the transformative power of transformers for capturing intricate temporal-spatial features and the importance of multi-source data and deep learning techniques for optimizing cell site management in the B5G era. Full article

We investigated the impact of surface treatments on Si-based electrolyte-gated transistors (EGTs) for detecting urea. Three types of EGTs were fabricated with distinct gate electrodes (Ag, Au, Pt) using a top-down method. These EGTs exhibited exceptional intrinsic electrical properties, including a low subthreshold swing of 80 mV/dec, a high on/off current ratio of 10⁶, and negligible hysteresis. Three surface treatment methods ((3-amino-propyl) triethoxysilane (APTES) and glutaraldehyde (GA), 11-mercaptoundecanoic acid (11-MUA), 3-mercaptopropionic acid (3-MPA)) were individually applied to the EGTs with different gate electrodes (Ag, Au, Pt). Gold nanoparticle binding tests were performed to validate the surface functionalization. We compared their detection performance of urea and found that APTES and GA exhibited the most superior detection characteristics, followed by 11-MUA and 3-MPA, regardless of the gate metal. APTES and GA, with the highest pKa among the three surface treatment methods, did not compromise the activity of urease, making it the most suitable surface treatment method for urea sensing. Full article

Medical and wellness tourism has emerged as a pivotal sector with significant economic implications globally, especially after the COVID-19 pandemic. This study delves into the landscape of Saudi Arabia’s medical and wellness tourism, recognizing its importance as a key player in the tourism industry. The study aims to elevate this sector to new heights on the global stage by employing the Business Model Canvas (BMC) as a strategic tool. BMC allows for a comprehensive analysis of the medical tourism industry in Saudi Arabia, breaking down key elements across its nine blocks: key partners, key activities, key resources, value propositions, customer segments, channels, customer relationships, cost structure, and revenue streams. The results of this study shed light on the unique selling proposition (USP) as a crucial strategic step for Saudi Arabia to distinguish itself and enhance its position in the international medical tourism arena. By identifying and maximizing the unique aspects within each BMC block, the study presents a roadmap for Saudi Arabia, navigating the challenges and capitalizing on the potential of the medical and wellness tourism sector. This research serves as a guide, emphasizing the strategic importance of a well-defined business model to shape the future of medical and wellness tourism in Saudi Arabia and establish a prominent global presence. Full article

Fungi are eukaryotic organisms with relatively few pathogenic members dangerous for humans, usually acting as opportunistic infections. In the last decades, several life-threatening fungal infections have risen mostly associated with the worldwide extension of chronic diseases and immunosuppression. The available antifungal therapies cannot combat this challenge because the arsenal of compounds is scarce and displays low selective action, significant adverse effects, and increasing resistance. A growing isolation of outbreaks triggered by fungal species formerly considered innocuous is being recorded. From ancient times, natural substances harvested from plants have been applied to folk medicine and some of them recently emerged as promising antifungals. The most used are briefly revised herein. Combinations of chemotherapeutic drugs with natural products to obtain more efficient and gentle treatments are also revised. Nevertheless, considerable research work is still necessary before their clinical use can be generally accepted. Many natural products have a highly complex chemical composition, with the active principles still partially unknown. Here, we survey the field underlying lights and shadows of both groups. More studies involving clinical strains are necessary, but we illustrate this matter by discussing the potential clinical applications of combined carnosic acid plus propolis formulations. Full article

Accurately estimating vegetation biomass in urban forested areas is of great interest to researchers as it is a key indicator of the carbon sequestration capacity necessary for cities to achieve carbon neutrality. The emerging vegetation biomass estimation methods that use AI technologies with remote sensing images often suffer from arge estimating errors due to the diversity of vegetation and the complex three-dimensional terrain environment in urban ares. However, the high resolution of Light Detection and Ranging (i.e., LiDAR) data provides an opportunity to accurately describe the complex 3D scenes of urban forests, thereby improving estimation accuracy. Additionally, deep earning foundation models have widely succeeded in the industry, and show great potential promise to estimate vegetation biomass through processing complex and arge amounts of urban LiDAR data efficiently and accurately. In this study, we propose an efficient and accurate method called 3D-CiLBE (3DCity Long-term Biomass Estimation) to estimate urban vegetation biomass by utilizing advanced deep earning foundation models. In the 3D-CiLBE method, the Segment Anything Model (i.e., SAM) was used to segment single wood information from a arge amount of complex urban LiDAR data. Then, we modified the Contrastive Language–Image Pre-training (i.e., CLIP) model to identify the species of the wood so that the classic anisotropic growth equation can be used to estimate biomass. Finally, we utilized the Informer model to predict the biomass in the ong term. We evaluate it in eight urban areas across the United States. In the task of identifying urban greening areas, the 3D-CiLBE achieves optimal performance with a mean Intersection over Union (i.e., mIoU) of 0.94. Additionally, for vegetation classification, 3D-CiLBE achieves an optimal recognition accuracy of 92.72%. The estimation of urban vegetation biomass using 3D-CiLBE achieves a Mean Square Error of 0.045 kg/m², reducing the error by up to 8.2% compared to 2D methods. The MSE for biomass prediction by 3D-CiLBE was 0.06kg/m² smaller on average than the inear regression model. Therefore, the experimental results indicate that the 3D-CiLBE method can accurately estimate urban vegetation biomass and has potential for practical application. Full article

We explore an idea put forward many years ago by Zeldovich and Novikov concerning the existence of compact objects endowed with arbitrarily small mass. The energy density of such objects, which we call “ghost stars”, is negative in some regions of the fluid distribution, producing a vanishing total mass. Thus, the interior is matched on the boundary surface to Minkowski space–time. Some exact analytical solutions are exhibited and their properties are analyzed. Observational data that could confirm or dismiss the existence of this kind of stellar object are discussed. Full article

Widespread adoption of mpMRI has led to a decrease in the number of patients requiring prostate biopsies. ⁶⁸Ga-PSMA-11 PET/CT has demonstrated added benefits in identifying csPCa. Integrating the use of these imaging techniques may hold promise for predicting the presence of csPCa without invasive biopsy. A retrospective analysis of 42 consecutive patients who underwent mpMRI, ⁶⁸Ga-PSMA-11 PET/CT, prostatic biopsy, and radical prostatectomy (RP) was carried out. A lesion-based model (n = 122) using prostatectomy histopathology as reference standard was used to analyze the accuracy of ⁶⁸Ga-PSMA-11 PET/CT, mpMRI alone, and both in combination to identify ISUP-grade group ≥ 2 lesions. ⁶⁸Ga-PSMA-11 PET/CT demonstrated greater specificity and positive predictive value (PPV), with values of 73.3% (vs. 40.0%) and 90.1% (vs. 82.2%), while the mpMRI Prostate Imaging Reporting and Data System (PI-RADS) 4–5 had better sensitivity and negative predictive value (NPV): 90.2% (vs. 78.5%) and 57.1% (vs. 52.4%), respectively. When used in combination, the sensitivity, specificity, PPV, and NPV were 74.2%, 83.3%, 93.2%, and 51.0%, respectively. Subgroup analysis of PI-RADS 3, 4, and 5 lesions was carried out. For PI-RADS 3 lesions, ⁶⁸Ga-PSMA-11 PET/CT demonstrated a NPV of 77.8%. For PI-RADS 4–5 lesions, ⁶⁸Ga-PSMA-11 PET/CT achieved PPV values of 82.1% and 100%, respectively, with an NPV of 100% in PI-RADS 5 lesions. A combination of ⁶⁸Ga-PSMA-11 PET/CT and mpMRI improved the radiological diagnosis of csPCa. This suggests that avoidance of prostate biopsy prior to RP may represent a valid option in a selected subgroup of high-risk patients with a high suspicion of csPCa on mpMRI and ⁶⁸Ga-PSMA-11 PET/CT. Full article

This paper presents the reconstruction framework of the Roman mosaic with the central scene from the abduction of Europa. The mosaic depicting Europa, discovered in Ptuj (Slovenia) and dated from the second half of the third to the beginning of the fourth century AD, once decorated the representative room of a Roman villa. The experimental section addresses the materials and methods used in the 2D reconstruction of the mosaic, including the creation of line drawings of the mosaic based on the preserved part of the mosaic, photogrammetric acquisition, and the creation and processing of 1:1 raster reconstructions of the entire mosaic. This is followed by color management and interpretation approaches which allow the mosaic elements to be implemented in a 3D animation. The presented approaches could be implemented in the reconstruction process of other mosaics and archaeological objects with adaptations to the specifics of related objects. Full article

Iron redox flow batteries (IRFBs) are cost-efficient RFBs that have the potential to develop low-cost grid energy storage. Electrode kinetics are pivotal in defining the cycle life and energy efficiency of the battery. In this study, graphite felt (GF) is heat-treated at 400, 500 and 600 °C, and its physicochemical and electrochemical properties are studied using XPS, FESEM, Raman and cyclic voltammetry. Surface morphology and structural changes suggest that GF heat-treated at 500 °C for 6 h exhibits acceptable thermal stability while accessing the benefits of heat treatment. Specific capacitance was calculated for assessing the wettability and electrochemical properties of pristine and treated electrodes. The 600 °C GF has the highest specific capacitance of 34.8 Fg⁻¹ at 100 mV s⁻¹, but the 500 °C GF showed the best battery performance. The good battery performance of the 500 °C GF is attributed to the presence of oxygen functionalities and the absence of thermal degradation during heat treatment. The battery consisting of 500 °C GF electrodes offered the highest voltage efficiency of ~74%, Coulombic efficiency of ~94%, and energy efficiency of ~70% at 20 mA cm⁻². Energy efficiency increased by 7% in a battery consisting of heat-treated GF in comparison to pristine GF. The battery is capable of operating for 100 charge–discharge cycles with an average energy efficiency of ~ 67% for over 100 cycles. Full article

This study aimed to investigate the role of the thymus in influencing long-term outcomes of COVID-19 by comparing the thymic appearance in patients with and without COVID-19 pulmonary sequelae at chest computed tomography (CT). A total of 102 adult patients previously hospitalized for COVID-19 underwent a follow-up chest CT three months after discharge. Pulmonary sequelae and thymic appearance were independently assessed by two experienced radiologists. The thymus was detectable in 55/102 patients (54%), with only 7/55 (13%) having any kind of pulmonary sequelae, compared to 33 out of 47 (70%, p < 0.001) in patients without thymic visibility, as confirmed in age-stratified analysis and at logistic regression analysis, where thymic involution had a 9.3 odds ratio (95% CI 3.0–28.2, p < 0.001) for the development of pulmonary sequelae. These results support the hypothesis that thymic reactivation plays a protective role against adverse long-term outcomes of COVID-19. Full article

Two plates of nanostructured ferritic alloy NFA-1 were processed by ball milling atomized Fe-14Cr-3W-0.4Ti-0.2Y (wt.%) with FeO powders, canning, and hot-extrusion at 850 °C, followed by annealing and multipass cross-rolling at 1000 °C. This produces a severe (001) brittle cleavage texture on planes running parallel to the plate faces. In the first plate (P1), pre-existing microcracks (MCs) formed on the cleavage planes during cross-rolling. The second plate (P2) contained far fewer, if any, MCs. Here, we compare the tensile data for out-of-plane (S) and in-plane (L) tensile axis orientations, at temperatures from −196 °C to 800 °C. We also assess the tensile property differences between P1 and P2, and the effect of specimen size. The L-orientation strength and ductility were excellent; for example, the room temperature (RT) yield stress, σ_y ≈ 1042 ± 102 MPa, and the total elongation, ε_t ≈ 12.9 ± 1.5%. In contrast, the S-orientation RT σ_y ≈ 708 ± 57 MPa, and ε_t ≤ 0.2%. These differences were due to cleavage on the brittle (001) planes. Cleavage leads to beneficial delamination toughening, but is deleterious to deformation processing and through-wall heat transfer. Therefore, it is important to quantitatively characterize the pronounced NFA-1 strength anisotropy due to severe crystallographic texturing and cleavage fracture. Full article

Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians’ ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow. Full article