Massive Applications Research Articles

Precision oncology implementation in a regional-based health care system: A professional consensus to define the pathway

ObjectivePrecision Oncology requires deep changes in organizational settings but little evidence has been identified about the best strategy to guarantee the delivery of this innovation to patients. In the Italian health care system, high heterogeneity could jeopardize equal access opportunity for patients. Following a consensus method, we aim to define shared solutions to address these issues in clinical practice. MethodA Delphi RAND method was chosen to record the consensus among involved health care professionals in the Italian region of Friuli Venezia Giulia. The item generation phase was conducted following a bottom-up approach. ResultsTen statements were defined on the main topics that emerged from the direct observation of the current practice, focusing on Molecular Tumor Board organization, massive parallel sequencing technology application, laboratory report content and informed consensus submission. All the statements reached a strong consensus and have been shared with the health care government authorities of our region. ConclusionsThe direct observation of the current practice in different health care authorities allowed to define ten statements as solution proposals to solve the identified complexities. This methodological approach could be applied in different organizational models but aiming to achieve a homogeneous clinical outcome for patients.

Photoactive self-cleaning zinc oxychloride coatings

Zinc oxychloride cement (ZOC) is considered a promising quick-drying material for fabricating photoactive coatings composed of three components: zinc oxide (ZnO), zinc chloride (ZnCl2), and water. However, its massive application has not been implemented due to the lack of research about its stability and mechanical performance. Thus, this article aims to provide insights about the self-cleaning photoactivity of ZOC coatings incorporated with titanium dioxide (TiO2) nanoparticles to boost the photocatalytic performance. Two stoichiometries of ZnO:ZnCl:H2O (112 and 415) were studied to investigate the influence of the molar ratio of the components on the physical and chemical properties. According to these results, no significant differences were observed in the physical and chemical properties between the two stoichiometries studied; however, the mechanical properties were affected. The coatings crystallized as simonkolleite Zn5(OH)8Cl2·H2O with a laminar hexagonal morphology, show high surface area, and absorbs light in the UVA region. The self-cleaning activity was evaluated using four model pollutants: Rhodamine B, Methylene blue, Orange G, and Carbon under accelerated weathering conditions. All the coatings were photoactive to decompose the pollutants from their surface and the addition of TiO2 nanoparticles did not significantly enhance the self-cleaning efficiency, but it improves the mechanical performance of the coatings. These results could be associated with the fact that TiO2 works as filler for voids in the coatings without affecting its settings. The stability of the coatings was confirmed after the exposure to accelerated weathering at high humidity conditions, which places them as an attractive, low-cost, and easy to apply self-cleaning material.

Falsely elevated triglyceride and lipase levels due to hyperglycerolemia in a burn patient treated with topical silver sulfadiazine

Pseudohypertriglyceridemia is an overestimation of serum triglyceride levels, potentially leading to an inaccurate diagnosis of hypertriglyceridemia. This can result from hyperglycerolemia, which interferes with enzymatic measurement methods. Hyperglycerolemia may arise from drugs or genetic glycerol kinase defects. We present a case of a severely burned patient with very high triglyceride levels (up to 5,696 mg/dL) that was resistant to lipid-lowering treatment, identified during the work up for pancreatitis associated with elevated lipase levels. Despite the very high triglyceride levels reported in the laboratory results, the standing plasma showed clear plasma, with no significant peak of pre-beta lipoprotein observed on serum lipoprotein electrophoresis. Serum apolipoprotein B levels were low, and urine triglyceride levels were high. This case confirms that very high serum glycerol levels caused the falsely elevations of triglyceride and lipase levels, resulting from hyperglycerolemia induced by the massive topical application of silver sulfadiazine cream (containing glycerine and glyceryl stearate) to the severely burned wound, which interfered with the laboratory assays. Triglyceride and lipase levels dramatically decreased after reducing the dose of silver sulfadiazine cream. No genetic defects related to glycerol kinase deficiency were identified.

Power of Homomorphic Encryption in Secure Data Processing

Homomorphic encryption is a form of encryption that allows computations to be performed on encrypted data without first having to decrypt it. This paper presents a detailed discussion of HE, a critical component in the protection of data in today's technology-driven environment. First, homomorphic encryption and its terminology will be introduced and then development process from the beginning to the present state will be discussed. Different classes of homomorphic encryption and analysis of internal workings and architecture of homomorphic encryption will be discussed. The usefulness of this technology in ensuring privacy in sensitive areas is discussed, as well as the limitations that may hinder the technology's advancement, including computation intensity and data growth. The paper also reasserts the massive application of homomorphic encryption in data security and privacy, stressing the need to continue the advancement to overcome existing drawbacks and enhance the application of the technique. While moving vast distances within the digital arena, the optimization of homomorphic encryption remains the guiding light to our freedom and privacy online.

Design of a 240 elements antenna system for 5G massive MIMO applications with an inclined beam and two operating modes

A massive MIMO (multiple-input-multiple-output) antenna system for 5G base stations is developed, featuring 240 elements and 60 ports. The system supports two modes: individual port operation and network operation. It has a rectangular shape with dimensions of 28×16.8×0.32 cm³ and consists of three layers per side, with 15 ports per layer. Each port contains a 2×2 patch sub-array operating at 3.5 GHz, with beam tilting to generate uncorrelated radiation patterns. The system provides a measured bandwidth of 252 MHz, covering the 3.39–3.64 GHz band. Gain and envelope correlation coefficients are evaluated according to mMIMO parameters. A prototype is fabricated, and the results obtained through CST simulations are validated by experimental measurements.

Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective.

The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore, in silico methods that utilize machine learning techniques to predict the toxicity potentials of NMs are a promising alternative approach if regulatory confidence in them can be enhanced. Previous reviews and regulatory OECD guidance documents have discussed in detail how to build an in silico predictive model for NMs. Nevertheless, there is still room for improvement in addressing the ways to enhance the model representativeness and performance from different angles, such as data set curation, descriptor selection, task type (classification/regression), algorithm choice, and model evaluation (internal and external validation, applicability domain, and mechanistic interpretation, which is key to ensuring stakeholder confidence). This review explores how to build better predictive models; the current state of the art is analyzed via a statistical evaluation of literature, while the challenges faced and future perspectives are summarized. Moreover, a recommended workflow and best practices are provided to help in developing more predictive, reliable, and interpretable models that can assist risk assessment as well as safe-by-design development of NMs.

Local Evaluation of Large-scale Remote Sensing Machine Learning-generated Building and Road Dataset: The Case of Rwanda

Accurate and up-to-date building and road data are crucial for informed spatial planning. In developing regions in particular, major challenges arise due to the limited availability of these data, primarily as a result of the inherent inefficiency of traditional field-based surveys and manual data generation methods. Importantly, this limitation has prompted the exploration of alternative solutions, including the use of remote sensing machine learning-generated (RSML) datasets. Within the field of RSML datasets, a plethora of models have been proposed. However, these methods, evaluated in a research setting, may not translate perfectly to massive real-world applications, attributable to potential inaccuracies in unknown geographic spaces. The scepticism surrounding the usefulness of datasets generated by global models, owing to unguaranteed local accuracy, appears to be particularly concerning. As a consequence, rigorous evaluations of these datasets in local scenarios are essential for gaining insights into their usability. To address this concern, this study investigates the local accuracy of large RSML datasets. For this evaluation, we employed a dataset generated using models pre-trained on a variety of samples drawn from across the world and accessible from public repositories of open benchmark datasets. Subsequently, these models were fine-tuned with a limited set of local samples specific to Rwanda. In addition, the evaluation included Microsoft’s and Google’s global datasets. Using ResNet and Mask R‑CNN, we explored the performance variations of different building detection approaches: bottom-up, end-to-end, and their combination. For road extraction, we explored the approach of training multiple models on subsets representing different road types. Our testing dataset was carefully designed to be diverse, incorporating both easy and challenging scenes. It includes areas purposefully chosen for their high level of clutter, making it difficult to detect structures like buildings. This inclusion of complex scenarios alongside simpler ones allows us to thoroughly assess the robustness of DL-based detection models for handling diverse real-world conditions. In addition, buildings were evaluated using a polygon-wise comparison, while roads were assessed using network length-derived metrics.Our results showed a precision (P) of around 75% and a recall (R) of around 60% for the locally fine-tuned building model. This performance was achieved in three out of six testing sites and is considered the lowest limit needed for practical utility of RSML datasets, according to the literature. In contrast, comparable results were obtained in only one out of six sites for the Google and Microsoft datasets. Our locally fine-tuned road model achieved moderate success, meeting the minimum usability threshold in four out of six sites. In contrast, the Microsoft dataset performed well on all sites. In summary, our findings suggest improved performance in road extraction, relative to building extraction tasks. Moreover, we observed that a pipeline relying on a combination of bottom-up and top-down segmentation, while leveraging open global benchmark annotation dataset as well as a small number of samples for fine-tuning, can offer more accurate RSML datasets compared to an open global dataset. Our findings suggest that relying solely on aggregated accuracy metrics can be misleading. According to our evaluation, even city-level derived measures may not capture significant variations in performance within a city, such as lower accuracy in specific neighbourhoods. Overcoming the challenges of complex areas might benefit from exploring alternative approaches, including the integration of LiDAR data, UAV images, aerial images or using other network architectures.

Integration of improved meta-heuristic and machine learning for optimizing energy efficiency in additive manufacturing process

Additive manufacturing (AM) has been a vital element of smart manufacturing. The high energy intensity or environmental sustainability issue of AM, however, has posed a great challenge to the future massive application, particularly laser-based direct energy deposition (L-DED). This study aims to determine the optimal processing parameters for energy-saving without compromising the geometrical appearance. A quantification model for energy efficiency at the process level was established, with two energy efficiency indicators of AM process. Then, a meta-heuristic Mayfly algorithm, augmented with Bayesian technique and mutation strategies, was proposed to improve hyperparameters in a typical machine learning model (XGBoost). Based on the full factorial L-DED experiments, this study compared the improved XGBoost with four types of XGBoost derivatives via four algorithm evaluation metrics. Non-dominated sorting genetic algorithm II was adopted to optimize the processing parameters subject to the constraints of geometrical appearance. Results indicated that the proposed algorithm outperformed other XGBoost derivatives in terms of prediction accuracy and convergence rate. The energy efficiency could be improved by 76.35 J/g or 6.78 % on average while ensuring the geometry of the deposited layers. This study could help enhance energy-efficient additive manufacturing via proper processing parameters selection and facilitate the sustainability in AM domain.

A Benchmark Test of High-Throughput Atomistic Modeling for Octa-Acid Host–Guest Complexes

Years of massive applications of high-throughput atomistic modeling tools such as molecular docking and end-point free energy calculations in the drug industry and academic exploration have made them indispensable parts of hierarchical screening. While the similarities between host–guest and protein–ligand complexes lead to the direct extension of techniques for protein–ligand screening to host–guest systems, the practical performance of these hit identification tools remains unclear in host-–-guest binding. Recent reports on specific host–guest complexes suggest that the experience on the accuracy ladder accumulated from protein–ligand cases could be invalid in host–guest complexes, which makes it an urgent need to perform a systematic benchmark to secure solid numerical supports and guidance of practical setups. Concerning molecular docking, there still lacks a comprehensive benchmark considering popular docking programs. As for end-point reranking, quantitative and rigorous free energy estimation via end-point formulism requires establishing statistically meaningful measurements of uncertainties due to finite sampling, which is neglected or underestimated by a significant portion in almost all main-stream applications. Further, a face-to-face comparison between different screening tools is required for the design of a hierarchical workflow. To fill the above-mentioned critical gaps, in this work, using a dataset containing tens of host–guest complexes involving basket-like macromolecular hosts from the octa acid family, we extensively benchmark seven academic docking protocols and perform post-docking end-point rescoring with twenty protocols. The resulting comprehensive benchmark provides conclusive pictures of the practical value of docking and end-point screening in OA host–guest binding.

Redefining the Skills Required on the Labour Market in the Context of the Development of Artificial Intelligence Systems. Case Study on Finnish Universities

The rise of AI technology across different sectors has drastically influenced jobs and employment overall. This paper's goal is to identify and redefine the fundamental skills that are needed in the job market in relation to the massive application of artificial intelligence. The methodology used includes a case study and document analysis. The case study is centred on the Finnish universities that have implemented a project to connect academic training and professional requirements in the era of artificial intelligence. The results of this paper suggest a radical shift from the usual and well-known labour-oriented competencies to a far more complex cognitive and interdisciplinary array of skills like complex problem-solving, emotional intelligence, and flexibility. Most importantly, the case study that this paper brings forward underscores that the educational process and innovation must be sustained in the job market because technology can never be stagnant and is continuously redrawing the boundaries of the job's responsibilities. In a nutshell, the competencies required in the labour market will have to be recast with the integration and development of artificial intelligence systems. Such developments need proactive actions from stakeholders, especially educational institutions, through which a proper channel is established for the smooth coexistence of the workforce in the future headed by artificial intelligence.

Virtual screening of cucurbituril host-guest complexes: Large-scale benchmark of end-point protocols under MM and QM Hamiltonians

The similarity of protein–ligand and host–guest complexes leads to the direct extension of end-point free energy techniques to virtual screening of the latter. Despite the massive application of end-point calculations in modelling and predictions of host–guest binding, it still lacks a large-scale benchmark test with sufficient sizes, sampling length and diverse coverages of chemical spaces to get rid of many disturbing factors and provide an unbiased statistical perspective of practical performance and value of end-point screening. Cucurbiturils as one of the main-stream macrocycles are quite popular in pharmaceutical research and industrial applications as drug reservoirs and carriers. In this work, we gather a large dataset of ∼150 host–guest complexes involving cucurbiturils with varying numbers of repeating units, extensively sample the conformational space in physical end states with both the single- and three-trajectory protocols, and extract the affinity estimates with a variety of MM and QM/GBSA Hamiltonians with implicit solvents. The diversity of chemical compositions, the quality of configurational sampling, and the comprehensiveness of parameter combinations are unprecedently high, enabling us to deduce conclusive pictures of the end-point performance in host–guest binding. Unlike previous reports on improved performance due to the incorporation of the QM treatment, shifting to QM/GBSA treatments deteriorates both the reproduction of absolute affinities and the prediction of affinity ranks in our large-scale end-point benchmark, regardless of the employed sampling protocol (i.e., single- and three-trajectory realization). Overall, in the end-point screening of cucurbituril host–guest complexes, we recommend sticking to the original MM/GBSA Hamiltonians in both sampling and free energy estimation. The best/recommended selection with high accuracy and robustness among all end-point protocols benchmarked in this work is the three-trajectory GAFF + GBOBC-I regime, which achieves top performances for both the reproduction of absolute affinities and ranking different host–guest pairs. Docking estimates with seven main-stream protocols have been observed as a promising tool for host–guest binding, but their ranking powers in the current large host–guest dataset does not surpass end-point tools, validating the value of post-docking end-point screening.

Synthesis of High-Quality Bulk Single-Crystal Black Phosphorus by the Circulating Vapor Growth Approach.

Black phosphorus (BP), a promising two-dimensional (2D) layered semiconductor material, has gained enormous attention due to its impressive properties over the past several years. Although plenty of methods have been developed to synthesize high-quality BP, most of the currently available BP materials still suffer from unsatisfactory crystallization, purity, and stability in air, hindering their practical application. A facile approach to synthesizing ultrahigh-quality single-crystal BP is of significance to shed light on the nature of 2D semiconductor materials and their massive application. In this work, we present the facile and efficient circulating vapor growth approach to growing bulk single-crystal BP. The as-grown BP material features high crystallinity and ultrahigh purity (higher than 99.999 at %), exceeding those of all the previously reported and some commercially available BP crystals. It also maintains excellent stability in air and water after 15 consecutive days of test. Moreover, the as-synthesized BP material features good thermal stability, oxidation resistance, and excellent electrical properties, as well. This study provides a new approach for the fabrication of ultrahigh-quality BP material and thus promotes its application.

A comprehensive power quality assessment method based on data envelopment analysis method and mutation decision-making

Abstract As the construction of new power systems continues to advance, integrating a high percentage of new energy sources into the grid and the massive application of power electronic equipment brings serious power quality problems. At the same time, power quality assessment is the basis for solving power quality problems. A comprehensive power quality assessment method combining data envelopment analysis and mutation decision-making is proposed in this paper. Firstly, a power quality governance efficiency model using data envelopment analysis is constructed. Secondly, a power quality assessment method is proposed, combining governance results and mutation decision-making. Finally, the efficiency value and power quality value of each assessment point are obtained to realize the governance efficiency and power quality ranking of the assessment points. The example results prove the effectiveness of the proposed method.

Theoretically predicted innovative palladium stripe dopingcobalt(1 1 1) surface with excellent catalytic performance for carbon monoxide oxidative coupling to dimethyl oxalate

Pd-based catalysts are extensively employed to catalyze CO oxidative coupling to generate DMO, while the expensive price and high usage of Pd hinder its massive application in industrial production. Designing Pd-based catalysts with high efficiency and low Pd usage as well as expounding the catalytic mechanisms are significant for the reaction. In this study, we theoretically predict that Pd stripe doping Co(1 1 1) surface exhibits excellent performance than pure Pd(1 1 1), Pd monolayer supporting on Co(1 1 1) and Pd single atom doping Co(1 1 1) surface, and clearly expound the catalytic mechanisms through the density functional theory (DFT) calculation and micro-reaction kinetic model analysis. It is obtained that the favorable reaction pathway is COOCH3–COOCH3 coupling pathway over these four catalysts, while the rate-controlling step is COOCH3+CO+OCH3→2COOCH3 on Pd stripe doping Co(1 1 1) surface, which is different from the case (2COOCH3→DMO) on pure Pd(1 1 1), Pd monolayer supporting on Co(1 1 1) and Pd single atom doping Co(1 1 1) surface. This study can contribute a certain reference value for developing Pd-based catalysts with high efficiency and low Pd usage for CO oxidative coupling to DMO.

Prospective life cycle assessment to support eco-design of solid oxide fuel cells

ABSTRACT Solid oxide fuel cells (SOFCs) could have great application potential and technological development. However, there are no studies that have quantitatively and rigorously estimated the environmental impacts in the future scenario. This study fills this gap through an innovative approach consisting of patent-based technological forecasting and prospective life cycle assessment (LCA). The analysis of the 58 selected patents reveals that future SOFCs could have (on average) +53% specific power which could lead to a 56% mass reduction compared to current SOFC. The prospective LCA shows an average global warming potential (GWP) reduction of 50%. The future tubular layout is more sustainable than planar one by about 15%. GWP decreases with increasing specific power and in cells with smaller sizes and thicknesses. Finally, the ductile future SOFCs, dedicated to mobile applications and dynamic loads, have a GWP greater than future stationary SOFCs, but still equal to half of the current SOFCs. All these results therefore confirm the potential of the patented SOFC developments on environmental sustainability, arguing in favour of their industrial development and a more massive application in the future.

Optofluidic chip with directly printed polymer optical waveguide Mach-Zehnder interferometer sensors for label-free biodetection

Optofluidic devices hold great promise in biomedical diagnostics and testing because of their advantages of miniaturization, high sensitivity, high throughput, and high scalability. However, conventional silicon-based photonic chips suffer from complicated fabrication processes and less flexibility in functionalization, thus hindering their development of cost-effective biomedical diagnostic devices for daily tests and massive applications in responding to public health crises. In this paper, we present an optofluidic chip based on directly printed polymer optical waveguide Mach-Zehnder interferometer (MZI) sensors for label-free biomarker detection. With digital ultraviolet lithography technology, high-sensitivity asymmetric MZI microsensors based on a width-tailored optical waveguide are directly printed and vertically integrated with a microfluidic layer to make an optofluidic chip. Experimental results show that the sensitivity of the directly printed polymer optical waveguide MZI sensor is about 1695.95 nm/RIU. After being modified with capture molecules, i.e., goat anti-human immunoglobulin G (IgG), the polymer optical waveguide MZI sensors can on-chip detect human IgG at the concentration level of 1.78 pM. Such a polymer optical waveguide-based optofluidic chip has the advantages of miniaturization, cost-effectiveness, high sensitivity, and ease in functionalization and thus has great potential in the development of daily available point-of-care diagnostic and testing devices.

Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks

5G is the fifth-generation technology standard for the new generation of cellular networks. Combining 5G and millimeter waves (mmWave) gives tremendous capacity and even lower latency, allowing you to fully enjoy the 5G experience. 5G is the successor to the fourth generation (4G) which provides high-speed networks to support traffic capacity, higher throughput, and network efficiency as well as supporting massive applications, especially internet-of-things (IoT) and machine-to-machine areas. Therefore, performance evaluation and analysis of such systems is a critical research task that needs to be conducted by researchers. In this paper, a new model structure of an urban-suburban environment in a 5G network formed of seven cells with a central urban cell (Hot spot) surrounded by six suburban cells is introduced. With the proposed model, the end-user can have continuous connectivity under different propagation environments. Based on the suggested model, the related capacity bounds are derived and the performance of 5G network is studied via a simulation considering different parameters that affect the performance such as the non-orthogonality factor, the load concentration in both urban and suburban areas, the height of the mobile, the height of the base station, the radius, and the distance between base stations. Blocking probability and bandwidth utilization are the main two performance measures that are studied, however, the effect of the above parameters on the system capacity is also introduced. The provided numerical results that are based on a network-level call admission control algorithm reveal the fact that the investigated parameters have a major influence on the network performance. Therefore, the outcome of this research can be a very useful tool to be considered by mobile operators in the network planning of 5G.

A DQN-based traffic classification method for mobile application recommendation with continual learning

With the popularity and development of smartphones, many mobile applications of various types have emerged. How to recommend mobile applications that match the user’s preferences and usage habits among the massive applications is a problem that needs to be solved. Traditional mobile application recommendation methods cannot dynamically track user behavior and preference changes in time and cannot timely correct the recommendation model, resulting in poor recommendation effects. The continual update of mobile applications will also invalidate the recommendation model based on traffic classification. To solve these problems, this paper proposes A Deep Q-Network (DQN) based traffic classification method for mobile application recommendation with continual learning, which embeds a DQN-based traffic classification model in the mobile terminal and sets up a reward and punishment mechanism to achieve self-supervised learning. By continuously adjusting and optimizing the model, the effectiveness of the traffic classification model is ensured, and the recommendation model is provided with accurate and reliable user behavior data support. Experiments on the ISCX and private datasets show that the proposed method performs better and can effectively guarantee the accuracy of the classification model.

Analysis of Performance Improvement of Planar Antenna Arrays With Optimized Square Rings for Massive MIMO Applications

The power pattern of the conventional fully optimized planar arrays can be properly reshaped according to the required user-defined constraints. However, the practical implementation of such fully optimized large planar arrays is complex and expensive. This paper introduces a new and efficient technique that is capable of providing better performance and almost the same power pattern shapes as that of the conventional fully optimized planar arrays by optimally adjusting the element amplitude and phase excitations of the outer-square rings instead of all elements' excitations. The proposed technique starts with a massive fully planar array then divides it into two contiguous sub-planar arrays which are both symmetric about the original array center. The elements excitation amplitudes or phases of the outer sub-planar array are only adjusted to form the desired power pattern shapes, while the amplitudes or phases of the central sub-planar array elements which have usually higher weights than the outer elements are made constants (i.e., they made ones for the case of amplitude-only control and zeros for the phase-only control). The results demonstrate the capability of the proposed planar array to form the required power patterns with far less number of the adjustable elements.

Development of a metasurface-based slot antenna for 5G MIMO applications with minimized cross-polarization and stable radiation patterns through mode manipulation

This paper presents an approach for designing metasurface antennas using the characteristic mode analysis method for 5G mm-wave multiple input–multiple output (MIMO) systems. The proposed metasurface antenna consists of a 3 × 3 array of modified patches with additional slits and stubs, which are fed by a coupling slot. This configuration reshapes surface currents and improves the radiation performance across a broad frequency range. The design offers significant advantages such as reduced antenna size, minimized influence of higher-order modes, and maintained low cross-polarization (XP) level. Experimental results demonstrate that the proposed metasurface-based slot antenna provides a bandwidth of 29.6% (23–31 GHz) with a return loss better than 10 dB. It achieves a peak gain of 9.43 dB and exhibits an XP level below − 26 dB and − 48 dB at φ=0∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi = 0^{ \\circ }$$\\end{document} and φ=90∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi = 90^{ \\circ }$$\\end{document} planes, respectively. The physical dimensions of the antenna are 0.9λ0 × 0.9λ0 × 0.08λ0, where λ0 is the free space wavelength at 27 GHz, resulting in an approximately 41% reduction compared to the conventional metasurface patch antenna. Moreover, the design proves to be well-suited for MIMO systems, enabling close placement of antenna elements without degrading their radiation patterns. The experimental results in 1 × 2 and 2 × 2 MIMO configurations represent that the isolation between antenna elements are better than 18 dB and 21 dB, respectively. The performance of the antennas remains stable in both configurations, effectively addressing concerns such as beam squint and eliminating the common issue of beam splitting observed in conventional metasurface MIMO antennas. Moreover, the envelope correlation coefficient value in both MIMO configurations is lower than 0.003. This significant advancement offers a promising solution for compact 5G mm-wave massive MIMO applications.