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Maraging steels are used in industries that require high precision due to their exceptional mechanical properties. This paper presents the impact of ten process parameters on the mechanical properties of maraging steel produced via electron beam melting (EBM). The parameters studied here include printing orientation, scanning speed, beam power, beam diameter, line offset, layer thickness, scan strategy, number of contours, preheating temperature, and post-processing. The mechanical properties evaluated are tensile strength, strain at fracture, elongation at failure, energy density, material hardness, porosity, and surface morphology. The results showed a significant correlation between these mechanical properties and the EBM parameters. The highest ultimate tensile stress, determined from the tensile test, was recorded at 1491.51 MPa, corresponding to an energy density of 64.28 J/mm3. The optimal parameters were identified as a line offset of 0.1 mm, a layer thickness of 50 μm, a scanning speed of 2800 mm/s, and a beam power of 15 mA. The energy density during EBM in turn affected microstructure, porosity, and mechanical properties. Furthermore, the study revealed that aging heat treatment had a considerable impact on the mechanical properties, with the highest tensile strength observed for a sample that had undergone aging at 460 °C for 8 h. The results show the critical role of process parameters and aging heat treatment in achieving optimal mechanical properties in EBM-produced maraging steel M789. These findings contribute to the optimization of this manufacturing technique for industrial applications. [ABSTRACT FROM AUTHOR]

Lattice structures are very interesting since they present good mechanical properties coupled with lightness. Today, the maturity reached by additive manufacturing technologies allows the production of such structures. However, the mechanical properties of the constituting material can be affected by the process itself, because of the particular geometry. In fact, in a previous work, the mechanical characteristics of the bulk‐printed material were used for modelling the bending behavior of lattice‐cored specimens made of Ti6Al4V and produced through electron beam melting (EBM) process, but a certain discrepancy with experimental results was found. Therefore, in this work, a procedure was proposed to determine the mechanical properties of the material the lattice specimen is made of, in order to reduce the gap between the numerical and the experimental results. By considering the redetermined yield strength, such mismatch was reduced. Moreover, the metallographic analysis of the material found that the α laths of the α + β microstructure, typical of the studied alloy, were thinner than those of bulk specimens, and the presence of α′ martensite was discovered too. The fracture surface analysis determined a ductile failure mode for the lattice core while a fragile mode for the skins. Highlights: A procedure to calculate the effective material properties was proposed.The numerical model for bending behavior resulted.A different microstructure for lattice material than bulk one was found.Damaging mechanisms were identified through SEM analysis. [ABSTRACT FROM AUTHOR]

In recent years, additive technologies have become increasingly important for the production of parts with complex geometries, enabling the rapid and efficient creation of objects with different shapes and configurations in industrial sectors such as medicine, aerospace and construction. The focus of the study was the analysis of arc processes for additive manufacturing, particularly through non-consumable electrode welding in an inert gas environment (TIG) and its applications as a heat source. Modern methods of 3D printing metal products, such as SLM (Selective Laser Melting), EBM (Electron Beam Melting), and LMD (Laser Metal Deposition), allow the production of parts with good quality indicators: accuracy, surface roughness, mechanical properties, and others. However, these methods are expensive due to the technological complexity of the equipment, and a weakness of these methods is their low productivity compared to arc methods. The results presented in this article show that the productivity of arc methods in additive manufacturing is several times higher than that of SLM, EBM, and LMD, and arc methods are more cost-effective due to lower equipment costs and reduced energy consumption. The article presents the schemes of arc methods of additive manufacturing. One of the promising directions in the development of hybrid technology, namely the use of the TIG heat source for sintering metal powders, offers an effective way to reduce the cost of additive manufacturing by replacing the laser as a heat source, while allowing the continued use of various types of metal powders, reinforcing materials, and metal-ceramic blends.

The activity of part orientation is of crucial importance in terms of impact on the quality of parts in powder bed additive manufacturing. To support the decision-making of computer aided manufacturing (CAM) operators, several knowledge and software models are proposed. Even if they seem to help operators in orientation process, their operational effectiveness still needs to be scientifically assessed. This paper proposes to evaluate the efficiency of a part orientation software in assisting CAM engineers in part orientation choice. The related software is based on the mathematization of action rules issued from a knowledge model. The knowledge involved in carrying out the orientation activity is analyzed based on the users' expertise. To do this, an empirical approach based on a case study is made with participants of different levels of skills. Several design scenarios including various part typologies to be oriented are submitted to engineers, before their manufacturing with electron beam melting (EBM) technology. Two means of orientation are thus used for comparison, namely, a manual orientation and a computer-aided orientation software. Based on the orientation results, an analysis of the software usage is undertaken. As an underlying result of our study, we have come up with an evaluation approach that can be reused in other contexts and with other software. [ABSTRACT FROM AUTHOR]

India is the most populous in the world. Its healthcare ecosystem and the spectrum of diseases seen make it the ideal place to carry out research. It also demands that practices are evidence-based to conserve precious healthcare resources. There are, however, challenges encountered in practicing evidence-based medicine in India. These include a lack of researchers trained in the practice of EBM, a paucity of funds in the research field as well as lack of familiarity among physicians with EBM. Training in the 5 steps of EBM, acronymized as the 5 As (Ask, Acquire, Appraise, Apply, and Assess) is essential for all healthcare professionals, It is critical to identify research gaps before undertaking any form of research to avoid unnecessary duplication. A systematic search of literature is useful in generating evidence maps which can help in identifying areas requiring further studies. The task before us is also to build an army of astute health care professionals armed with skills of EBM, who can separate the grain from the chaff and be the torchbearers of evidence-based practices. There is a need to encourage a culture of EBM, where it is the responsibility of every clinician to calibrate the most evidence-based solution to their setting and administer it taking into account the values and beliefs of the patient. That would be the point of the union of the science and art of evidence-based medicine.

Abstract Background Primary care depends upon a good information flow across professional and structural boundaries to provide the best care for patients. Previous research has mainly focused on Evidence-Based Practice (EBP) within specific professions. Mapping of pan-professional experiences of and attitudes to EBP in publicly funded clinical practice is necessary to deepen the understanding of EBP and its implementation. Thus, this study aimed to investigate healthcare professionals’ experiences of and attitudes towards working in accordance with EBP in primary care. Methods The study used a convergent mixed methods design divided into two strands: a quantitative enquiry tool (Evidence-Based Practice Attitude Scale, EBPAS) and a set of qualitative interviews analysed by means of qualitative content analysis. Both strands included all primary care employees with patient interaction in the studied county (n = 625), including doctors, nurses, physiotherapists, psychologists and assistant nurses. Out of the original 625 healthcare professionals, 191 finished the first strand and 8 volunteered for the second strand (2 nurses, 2 physiotherapists, 1 psychiatrist and 3 doctors). Results The EBPAS value of 2.8 (max 4) indicated a generally positive attitude towards EBP amongst the population, which was also evident in the interviews. However, there were additional experiences of not having the ability or resources to engage in EBP. This was illustrated by the theme that emerged from the qualitative content analysis: “The dilemma of the split between theory and reality”. Due to the organisational and managerial focus on efficiency rather than quality of care, there were few or no incentives for promoting individual educational or research development. Conclusions Although the general attitude towards EBP is positive, experiences of practising it differ. There is a need to increase knowledge of EBP concepts, requirements and implementation in the clinical setting. The absence of opportunities to do research and collegial debate about new ways of finding and implementing research-based evidence results might influence the quality of care.

Subject of the study Clinical practice guidelines are an effective strategy for optimizing health care by promoting evidence-based recommendations. Appropriate methodology and a rigorous strategy when formulating the recommendations of clinical guidelines has a significant impact on their quality, reliability and ability to be effectively implemented in practice. Purpose of the study The purpose of our study was to assess the quality of clinical practice guidelines for asthma management using the AGREE II tool. The analysis compared the quality of Polish clinical guidelines with those from other countries and international organizations. Nine guidelines were evaluated: Management of Asthma Exacerbations in Adults - Guidelines for General Practitioners (author: Supreme Medical Council, Poland), Global Strategy for Asthma Management and Prevention (author: The Global Initiative for Asthma (GINA)), Asthma and Respiratory Foundation NZ Adolescent and Adult Asthma Guidelines 2020: a quick reference guide (author: Asthma and Respiratory Foundation New Zealand), Australian Asthma Handbook V2.0 (author: National Asthma Council Australia), National Clinical Guideline: The Diagnosis and Management of Asthma in Adults V2. 1 (author: Ministry of Public Health Qatar), SIGN 158 British guideline on the management of asthma (author: Scottish Intercollegiate Guidelines Network (SIGN) British Thoracic Society), Management of Asthma in Adults (author: Malaysian Health Technology Assessment Section (MaHTAS)), Asthma: diagnosis, monitoring and chronic asthma management (author: National Institute for Health and Care Excellence (NICE)), and The Spanish Guideline on the Management of Asthma (author: Guía Española para el Manejo del Asma (GEMA)). Results The highest scored domains were transparency and presentation of recommendations (all guidelines scored 100% or close to 100%), guideline scope and purpose, and editorial independence. The domains that scored lowest were in the areas of stakeholder engagement and applicability (79.3% and 69.2%, respectively). The largest differences were observed in the area of editorial independence, ranging from 100% (the maximum possible score) to 16.7%. The guidelines developed by the National Institute for Health and Care Excellence and the Scottish Intercollegiate Guidelines Network received the highest overall score. The Polish guidelines received the maximum number of points in areas related to scope and purpose, transparency and presentation, and editorial independence. Conclusions A properly conducted guideline development process, including the involvement of experts and adherence to methodological standards, results in high-quality clinical practice guidelines. High-quality clinical practice guidelines can be used as tools to support clinical decision-making, however, guideline authors should pay special attention to the applicability of guidelines in practice and the involvement of all stakeholders. To improve the process of guideline development and patient access to the newest therapies, it is also important to consider the synergies between the recommendations in clinical practice guidelines and health technology assessment reports.

Obesity has been a global health problem since the twentieth century. Despite the intensive research, there is no scientific consensus on the onset of obesity. The energy balance model (EBM) and the carbohydrate-insulin model (CIM) are two competing obesity theories, each with supporting and conflicting evidence. In this essay, I propose a new model, the energy-rush and insulin model (ERIM) which integrates not only the energy intake and expenditure, but also the food composition and digestibility, to explain how the high energy-rush and insulin secretion contribute to the development of obesity. The ERIM offers a novel framework to explain how obesity occurs and proposes new recommendations which may reverse the obesity epidemic in the future.

With the growth of global population and economic development, people are facing the problem of increasing scarcity of renewable energy and unsustainable energy use. To achieve the sustainable development goals (SDGs) proposed by the United Nations, research on energy consumption efficiency has become particularly important. This research evaluates the energy consumption efficiency of 270 cities in China through an improved EBM model and finds a common phenomenon of low energy consumption efficiency in the cities, with the highest efficiency in northeast China and the lowest efficiency in eastern China. In addition, the efficiency of industrial exhaust emissions most significantly positively correlates with the efficiency of employed population and total energy consumption efficiency, while the efficiency of regional GDP does not significantly correlate with the efficiency of the two input variables. Using the LMDI method to decompose the driving factors of energy consumption efficiency in the cities, we find that the most important factor affecting energy consumption efficiency is their own energy endowment. Therefore, to improve the energy consumption efficiency of its cities, the China government should comprehensively consider factors such as regional economic development level, industrial structure, and technological level differences, formulate relevant energy-saving and emission-reduction policies, focus on optimizing the energy consumption structure, encourage technological progress and innovation, and help increase investment in science and technology. [ABSTRACT FROM AUTHOR]

Randomized controlled trials (RCTs) are considered to produce the highest level of evidence in the original studies that informs the practice of evidence-based medicine (EBM). By manipulating an independent variable to study its impact on the outcome, RCTs establish causal relationships and provide valuable insights into clinical treatment. To improve patient outcomes and optimize the use of clinical resources, the practice of EBM plays a crucial role in designing and conducting RCTs to evaluate the effectiveness of clinical interventions. This review aims to explore the essential steps involved in conducting a rigorous and reliable RCT, ensuring the generation of highquality evidence. [ABSTRACT FROM AUTHOR]

Hybrid manufacturing (HM) is a process that combines additive manufacturing (AM) and subtractive manufacturing (SM). It is becoming increasingly recognized as a solution capable of producing components of high geometric complexity, while at the same time ensuring the quality of the surface finish, rigour and geometric tolerance on functional surfaces. This work aims to study the surface finish quality of an orthopaedic hip resurfacing prosthesis obtained by HM. For this purpose, test samples of titanium alloy Ti-6Al-4V using two Power Bed Fusion (PBF) processes were manufactured, which were finished by turning and 5-axis milling. It was verified that, upon the machining tests, no differences in Ra and Rt were found between the various types of AM. Regarding the type of SM used, 5-axis milling provided lower roughness results with a consistent value of Ra = 0.6 µm. The use of segmented circle mills in 5-axis milling proved to be an asset in achieving a good surface finish. This work successfully validated the concept of HM to produce a medical device, namely, an orthopaedic hip prosthesis. As far as surface quality is concerned, it could be concluded that the optimal solution for this case study is 5-axis milling. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has become increasingly integral to healthcare, aiding medical professionals in disease diagnosis. However, a critical challenge faced by practitioners lies in the trustworthiness of diagnosed results, as machine learning models often lack transparent explanations for their predictions. Explainable AI (XAI) addresses this, allowing practitioners and patients to comprehend and trust outcomes. This study employs the liver disease patient dataset to predict liver disease, vital in India where yearly, 10 lakh people face new liver cirrhosis diagnoses with high mortality. Early detection difficulties underscore the need for transparent predictions. Using Kaggle data, Random Forest, XGBoost, and Explainable Boosting Machine (EBM) classifiers are compared, with EBM outshining, achieving a 99.8% accuracy score. The study concludes that the EBM classifier is a superior and transparent liver disease predictor. [ABSTRACT FROM AUTHOR]

Abstract Objectives Decision aids (DAs) are promising tools to foster evidence‐based shared decision‐making between practitioners and service users. Nevertheless, it is still obscure how an evidence‐based DA for people with severe mental illness, especially psychosis, should look in an inpatient treatment setting to be useful and feasible. Therefore, we conducted focus groups with psychiatrists and service users to collect and assess their expectations and wishes regarding an evidence‐based DA. From these findings, we derived immediate recommendations for the future development of DAs. Methods We held two group interviews with service users (n = 8) and three group interviews with psychiatrists (n = 10). We used an open, large‐scale topic guide. First, we presented data from a current meta‐analysis on antipsychotics to the interviewees and, in a second step, asked for their expectations and wishes towards a DA that integrates these data. Results Our thematic analysis revealed six key themes addressed by the respondents: (1) general considerations on the importance and usefulness of such a DA, (2) critical comments on psychiatry and psychopharmacotherapy, (3) communicative prerequisites for the use of a DA, (4) form and content of the DA, (5) data input, data processing and output as well as (6) application of the DA and possible obstacles. Conclusions Participants identified several important features for the development of DAs for selecting antipsychotics in inpatient psychiatric treatment. The digital format was met with the greatest approval. Especially the adaptability to different needs, users and psychopathologies and the possibility to outsource information dissemination via app seemed to be a decisive convincing argument. Further research is required to test specific features of DAs to be developed in clinical settings.

This study investigates the elasticity of specific lattice structures made from titanium alloy (Ti6Al4V), namely, anatomically shaped lattice scaffolds (ASLS) aimed for reinforcement of the bone tissue graft that substitute a missing piece of the previously injured bone during its recovery. ASLSs that were used for testing were fabricated using the Electron Beam Melting (EBM) method. The mechanical properties of the ASLS were examined through uniaxial compression tests. Compression testing revealed the complex non-linear behavior of the scaffold structure's elasticity, with distinct compression stages and deformation dependencies. The ASLS structures exhibited quasi-elastic deformation followed by the rupture of individual struts. Results demonstrate that the ASLSs can be stiffened by applying appropriate compression load and accordingly achieve the target elasticity of the structure for the specific load range. The modulus of elasticity was determined for different compression stages of ASLS, allowing interpolation of the functional relation between the modulus of elasticity and compressive force that is used for stiffening the ASLS. This study enhances the understanding of the mechanical behavior of the specific lattice structures made of Ti6Al4V and provides insights for the development of mechanically optimized anatomically shaped lattice scaffolds. [ABSTRACT FROM AUTHOR]

Objective To train and test a model predicting chronic kidney disease (CKD) using the Generalized Additive2 Model (GA2M), and compare it with other models being obtained with traditional or machine learning approaches. Materials We adopted the Health Search Database (HSD) which is a representative longitudinal database containing electronic healthcare records of approximately 2 million adults. Methods We selected all patients aged 15 years or older being active in HSD between January 1, 2018 and December 31, 2020 with no prior diagnosis of CKD. The following models were trained and tested using 20 candidate determinants for incident CKD: logistic regression, Random Forest, Gradient Boosting Machines (GBMs), GAM, and GA2M. Their prediction performances were compared by calculating Area Under Curve (AUC) and Average Precision (AP). Results Comparing the predictive performances of the 7 models, the AUC and AP for GBM and GA2M showed the highest values which were equal to 88.9%, 88.8% and 21.8%, 21.1%, respectively. These 2 models outperformed the others including logistic regression. In contrast to GBMs, GA2M kept the interpretability of variable combinations, including interactions and nonlinearities assessment. Discussion Although GA2M is slightly less performant than light GBM, it is not "black-box" algorithm, so being simply interpretable using shape and heatmap functions. This evidence supports the fact machine learning techniques should be adopted in case of complex algorithms such as those predicting the risk of CKD. Conclusion The GA2M was reliably performant in predicting CKD in primary care. A related decision support system might be therefore implemented. [ABSTRACT FROM AUTHOR]

Titanium alloys additively manufactured by electron beam melting (EBM) inevitably obtained some pore defects, which significantly reduced the very high cycle fatigue performance. An ultrasonic fatigue test was carried out on an EBM TC21 titanium alloy with hot isostatic pressing (HIP) and non-HIP treatment, and the effect of pore defects on the very high cycle fatigue (VHCF) behavior were investigated for the EBM TC21 titanium alloy. The results showed that the S-N curve of non-HIP specimens clearly had a tendency to decrease in very high cycle regimes, and HIP treatment significantly improved fatigue properties. Fatigue limits increased from 250 MPa for non-HIP specimens to 430 MPa for HIP ones. Very high cycle fatigue crack mainly initiated from the internal pore for EBM specimens, and a fine granular area (FGA) was observed at the crack initiation site in a very high cycle regime for both non-HIP and HIP specimens. ΔKFGA had a constant trend in the range from 2.7 MPa m to 3.5 MPa m , corresponding to the threshold stress intensity factor range for stable crack propagation. The effect of pore defects on the very high cycle fatigue limit was investigated based on the Murakami model. Furthermore, a fatigue indicator parameter (FIP) model based on pore defects was established to predict fatigue life for non-HIP and HIP specimens, which agreed with the experimental data. [ABSTRACT FROM AUTHOR]

Objectives: Decision aids (DAs) are promising tools to foster evidence‐based shared decision‐making between practitioners and service users. Nevertheless, it is still obscure how an evidence‐based DA for people with severe mental illness, especially psychosis, should look in an inpatient treatment setting to be useful and feasible. Therefore, we conducted focus groups with psychiatrists and service users to collect and assess their expectations and wishes regarding an evidence‐based DA. From these findings, we derived immediate recommendations for the future development of DAs. Methods: We held two group interviews with service users (n = 8) and three group interviews with psychiatrists (n = 10). We used an open, large‐scale topic guide. First, we presented data from a current meta‐analysis on antipsychotics to the interviewees and, in a second step, asked for their expectations and wishes towards a DA that integrates these data. Results: Our thematic analysis revealed six key themes addressed by the respondents: (1) general considerations on the importance and usefulness of such a DA, (2) critical comments on psychiatry and psychopharmacotherapy, (3) communicative prerequisites for the use of a DA, (4) form and content of the DA, (5) data input, data processing and output as well as (6) application of the DA and possible obstacles. Conclusions: Participants identified several important features for the development of DAs for selecting antipsychotics in inpatient psychiatric treatment. The digital format was met with the greatest approval. Especially the adaptability to different needs, users and psychopathologies and the possibility to outsource information dissemination via app seemed to be a decisive convincing argument. Further research is required to test specific features of DAs to be developed in clinical settings. Patient or Public Contribution: The entire project is supported by an advisory board, including also experienced users. The members are regularly updated about the process and give important feedback. [ABSTRACT FROM AUTHOR]

The new generation of manufacturing methods that extends over subtractive type is introduced by additive manufacturing. The benefit of additive manufacturing is that it directly uses 3D CAD models to create three-dimensional objects by adding more layers of material and joining them together. It is commonly utilized in the motor sector, aerospace industry, biomedical applications, prototyping, fashion such as creation of custom-made jewellery, accessories, and even clothing and many more. Additive manufacturing technology is extensively employed because of its numerous benefits, which include multimaterial goods, improved product ergonomics, on-demand manufacturing, short production runs, and so on. Various fabricating methods, which include rapid prototyping, stereolithography, electron-beam melting, fused-deposition modelling, 3D printing (3DP), selective laser sintering, etc. Understanding the complex relationships between fundamental process parameters, flaws, and the finished product of the AM process depends heavily on mechanical testing. Because of the growing use of additive manufacturing in a variety of industries, it is critical to evaluate the mechanical performance of the components created. [ABSTRACT FROM AUTHOR]

Nonlinear oscillations are an essential fact in physical science, mechanical structures, and other engineering problems. Some of the popular analytical solutions to analyze nonlinear differential equations governing the behavior of strongly nonlinear oscillators are the Energy Balance Method (EBM), and He's Amplitude Frequency Formulation (HAFF). The lack of precision and accuracy despite needing several computational steps to resolve the system frequency is the main demerit of these methods. This research creates a novel analytical approximation approach with a very efficient algorithm that can perform the calculation procedure much easier and with much higher accuracy than classic EBM and HAFF. The presented method's steps rely on Hamiltonian relations described in EBM and the defined relationship between frequency and amplitude in HAFF. This paper demonstrates the substantial precision of the presented method compared to common EBM and HAFF applied in different and well-known engineering phenomena. For instance, the approximate solutions of the equations govern some strongly nonlinear oscillators, including the two-massspring systems, buckling of a column, and duffing relativistic oscillators are presented here. Subsequently, their results are compared with the Runge-Kutta method and exact solutions obtained from the previous research. The proposed novel approach resultant error percentages show an excellent agreement with the numerical solutions and illustrate a very quickly convergent and more precise than mentioned typical methods. [ABSTRACT FROM AUTHOR]

Innovative carbon reduction and sustainability solutions are needed to combat climate change. One promising approach towards cleaner air involves the utilization of lithium-ion batteries (LIB) and electric power vehicles, showcasing their potential as innovative tools for cleaner air. However, we must focus on the entire battery life cycle, starting with production. By prioritizing the efficiency and sustainability of lithium-ion battery manufacturing, we can take an essential step toward mitigating climate change and creating a healthier planet for future generations. A comprehensive case study of the leading LIB manufacturers demonstrates the usefulness of the suggested hybrid methodology. Initially, we utilized the Malmquist model to evaluate these firms' total efficiency while dissecting their development into technical and technological efficiency change components. We employed the Epsilon-Based Measure (EBM) model to determine each organization's efficiency and inefficiency scores. The findings show that the EBM approach successfully bridged the gap in the LIB industry landscape. Combined with the Malmquist model, the resulting framework offers a powerful and equitable evaluation paradigm that is easily applicable to any domain. Furthermore, it accurately identifies the top-performing organizations in specific aspects across the research period of 2018–2021. The EBM model demonstrates that most organizations have attained their top level, except for A10, which has superior technology adoption but poor management. A1, A2, A4, A6, A8, A9, and A10 were unable to meet their targets because of the COVID-19 pandemic, despite productivity improvements. A12 leads the three highest-scoring enterprises in efficiency and total productivity changes, while A3 and A5 should focus on innovative production techniques and improved management. The managerial implications provide vital direction for green energy practitioners, enhancing their operational effectiveness. Concurrently, consumers can identify the best LIB manufacturers, allowing them to invest in long-term green energy solutions confidently. [ABSTRACT FROM AUTHOR]

This study illustrates the utility and effectiveness of machine learning algorithms in identifying dependencies in data transmitted in industrial networks. The analysis was performed for two different algorithms. The study was carried out for the XGBoost (Extreme Gradient Boosting) algorithm based on a set of decision tree model classifiers, and the second algorithm tested was the EBM (Explainable Boosting Machines), which belongs to the class of Generalized Additive Models (GAM). Tests were conducted for several test scenarios. Simulated data from static equations were used, as were data from a simulator described by dynamic differential equations, and the final one used data from an actual physical laboratory bench connected via Modbus TCP/IP. Experimental results of both techniques are presented, thus demonstrating the effectiveness of the algorithms. The results show the strength of the algorithms studied, especially against static data. For dynamic data, the results are worse, but still at a level that allows using the researched methods to identify dependencies. The algorithms presented in this paper were used as a passive protection layer of a commercial IDS (Intrusion Detection System). [ABSTRACT FROM AUTHOR]

Featured Application: This article deals with a method for directly determining the volume loss during a pin-on-disc test. The results can provide a reliable basis for an in-depth study of the performance of prosthetic joints, including a tribo-pair consisting of a Ti6Al4V pin produced by EBM and a UHMWPE sheet. Debris formation is a crucial aspect that determines the lifespan of prosthetic joints. The wearing contact between ultra-high molecular weight polyethylene (UHMWPE) and a Ti alloy surface has been studied in the literature. However, when measuring mass loss, potential errors can arise due to the very small values involved (on the order of some units of 0.1 mg in experiments lasting several hours) and be caused by the absorption of humidity in the specimen, in addition to the lack of accuracy typical of weight scales. These errors can hardly be avoided, but accurate cleaning and drying processes can minimize them. With these premises, the present work aims to determine, by pin-on-disc test, the wear effect in the UHMWPE rotating sheet and Ti6Al4V pin produced by Electron Beam Melting (EBM) under dry and lubricated conditions. The morphology of the worn surface was documented by optical microscopy, and the volume loss of both the rotating specimens and the pin was accurately calculated through the detection of the wear track observed by optical microscopy. In particular, the present work proposes a method for directly determining the volume loss of the polymer to compare it with that obtained with the weight measurement. For both procedures, the uncertainty in evaluating the specific wear rate was analyzed, demonstrating that volume measurement allows for avoiding any possible error associated with weighing the polymeric specimens. [ABSTRACT FROM AUTHOR]

Despite the significant potential advantages of processing Ti-5Al-5Mo-5V-1Cr-1Fe alloy (Ti-55511) using Electron Beam Melting (PBF-EB/M), when compared to conventional manufacturing technologies, the resulting internal defects are an important characteristic of such additive technologies and can highly decrease mechanical properties. One of the most dangerous defects formed during metal additive manufacturing processes are material discontinuities such as a lack of fusion. Defects of this type, due to their "flat" nature, are difficult to characterize. For cycle-loaded specimens, where the loading force acts perpendicular to the lack-of-fusion plane, defects of this type can significantly reduce fatigue properties. This paper presents the results of research aimed at improving the fatigue properties of Ti55511 alloy by reducing the influence of the lack-of-fusion defect on fatigue damage. The static and fatigue properties of specimens in the as-built state, as well as after hot isostatic pressing (HIP) treatment, were analyzed. The effect of HIP on both the reduction of pores and the degree of sphericity when using the X-ray computed tomography (XCT) system was presented. The change in the microstructure after HIP was analyzed in terms of the change in the size of individual phases, as well as the change in the phase ratio. This paper also contains a fractographic analysis of the samples after tensile and fatigue tests. [ABSTRACT FROM AUTHOR]

Abstract Background Choosing an antipsychotic medication is an important medical decision in the treatment of schizophrenia. This decision requires risk-benefit assessments of antipsychotics, and thus, shared-decision making between physician and patients is strongly encouraged. Although the efficacy and side-effect profiles of antipsychotics are well-established, there is no clear framework for the communication of the evidence between physicians and patients. For this reason, we developed an evidence-based shared-decision making assistant (SDM-assistant) that presents high-quality evidence from network meta-analysis on the efficacy and side-effect profile of antipsychotics and can be used as a basis for shared-decision making between physicians and patients when selecting antipsychotic medications. Methods The planned matched-pair cluster-randomised trial will be conducted in acute psychiatric wards (n = 14 wards planned) and will include adult inpatients with schizophrenia or schizophrenia-like disorders (N = 252 participants planned). On the intervention wards, patients and their treating physicians will use the SDM-assistant, whenever a decision on choosing an antipsychotic is warranted. On the control wards, antipsychotics will be chosen according to treatment-as-usual. The primary outcome will be patients’ perceived involvement in the decision-making during the inpatient stay as measured with the SDM-Q-9. We will also assess therapeutic alliance, symptom severity, side-effects, treatment satisfaction, adherence, quality of life, functioning and rehospitalizations as secondary outcomes. Outcomes could be analysed at discharge and at follow-up after three months from discharge. The analysis will be conducted per-protocol using mixed-effects linear regression models for continuous outcomes and logistic regression models using generalised estimating equations for dichotomous outcomes. Barriers and facilitators in the implementation of the intervention will also be examined using a qualitative content analysis. Discussion This is the first trial to examine a decision assistant specifically designed to facilitate shared-decision making for choosing antipsychotic medications, i.e., SDM-assistant, in acutely ill inpatients with schizophrenia. If the intervention can be successfully implemented, SDM-assistant could advance evidence-based medicine in schizophrenia by putting medical evidence on antipsychotics into the context of patient preferences and values. This could subsequently lead to a higher involvement of the patients in decision-making and better therapy decisions. Trial registration German Clinical Trials Register (ID: DRKS00027316 , registration date 26.01.2022).

With the growth of global population and economic development, people are facing the problem of increasing scarcity of renewable energy and unsustainable energy use. To achieve the sustainable development goals (SDGs) proposed by the United Nations, research on energy consumption efficiency has become particularly important. This research evaluates the energy consumption efficiency of 270 cities in China through an improved EBM model and finds a common phenomenon of low energy consumption efficiency in the cities, with the highest efficiency in northeast China and the lowest efficiency in eastern China. In addition, the efficiency of industrial exhaust emissions most significantly positively correlates with the efficiency of employed population and total energy consumption efficiency, while the efficiency of regional GDP does not significantly correlate with the efficiency of the two input variables. Using the LMDI method to decompose the driving factors of energy consumption efficiency in the cities, we find that the most important factor affecting energy consumption efficiency is their own energy endowment. Therefore, to improve the energy consumption efficiency of its cities, the China government should comprehensively consider factors such as regional economic development level, industrial structure, and technological level differences, formulate relevant energy-saving and emission-reduction policies, focus on optimizing the energy consumption structure, encourage technological progress and innovation, and help increase investment in science and technology.

Artificial Intelligence (AI) has become increasingly integral to healthcare, aiding medical professionals in disease diagnosis. However, a critical challenge faced by practitioners lies in the trustworthiness of diagnosed results, as machine learning models often lack transparent explanations for their predictions. Explainable AI (XAI) addresses this, allowing practitioners and patients to comprehend and trust outcomes. This study employs the liver disease patient dataset to predict liver disease, vital in India where yearly, 10 lakh people face new liver cirrhosis diagnoses with high mortality. Early detection difficulties underscore the need for transparent predictions. Using Kaggle data, Random Forest, XGBoost, and Explainable Boosting Machine (EBM) classifiers are compared, with EBM outshining, achieving a 99.8% accuracy score. The study concludes that the EBM classifier is a superior and transparent liver disease predictor.

In this work, the mechanical properties, chemical composition, and surface morphology of hydroxyapatite coatings deposited by pulsed laser deposition on Ti6Al4V substrates manufactured by electron beam melting, varying the deposition energy, were studied. The microindentation and indentation tests carried out following the ASTM E384-17 standard allowed to obtain average hardness and microhardness values. Scanning electron microscopy was used to evaluate the substrate coating properties, thus, obtaining average grain size values of the hydroxyapatite coating. Chemical composition resulted from energy-dispersive X-ray spectroscopy analysis. Important characteristics that indicate the incidence of deposition parameters regarding the mechanical and morphological properties of the biocompatible coating on parts produced by additive manufacturing for use in bone implants were evaluated.

Titanium alloys additively manufactured by electron beam melting (EBM) inevitably obtained some pore defects, which significantly reduced the very high cycle fatigue performance. An ultrasonic fatigue test was carried out on an EBM TC21 titanium alloy with hot isostatic pressing (HIP) and non-HIP treatment, and the effect of pore defects on the very high cycle fatigue (VHCF) behavior were investigated for the EBM TC21 titanium alloy. The results showed that the S-N curve of non-HIP specimens clearly had a tendency to decrease in very high cycle regimes, and HIP treatment significantly improved fatigue properties. Fatigue limits increased from 250 MPa for non-HIP specimens to 430 MPa for HIP ones. Very high cycle fatigue crack mainly initiated from the internal pore for EBM specimens, and a fine granular area (FGA) was observed at the crack initiation site in a very high cycle regime for both non-HIP and HIP specimens. ΔKFGA had a constant trend in the range from 2.7 MPam to 3.5 MPam, corresponding to the threshold stress intensity factor range for stable crack propagation. The effect of pore defects on the very high cycle fatigue limit was investigated based on the Murakami model. Furthermore, a fatigue indicator parameter (FIP) model based on pore defects was established to predict fatigue life for non-HIP and HIP specimens, which agreed with the experimental data.

This study investigates the elasticity of specific lattice structures made from titanium alloy (Ti6Al4V), namely, anatomically shaped lattice scaffolds (ASLS) aimed for reinforcement of the bone tissue graft that substitute a missing piece of the previously injured bone during its recovery. ASLSs that were used for testing were fabricated using the Electron Beam Melting (EBM) method. The mechanical properties of the ASLS were examined through uniaxial compression tests. Compression testing revealed the complex non-linear behavior of the scaffold structure’s elasticity, with distinct compression stages and deformation dependencies. The ASLS structures exhibited quasi-elastic deformation followed by the rupture of individual struts. Results demonstrate that the ASLSs can be stiffened by applying appropriate compression load and accordingly achieve the target elasticity of the structure for the specific load range. The modulus of elasticity was determined for different compression stages of ASLS, allowing interpolation of the functional relation between the modulus of elasticity and compressive force that is used for stiffening the ASLS. This study enhances the understanding of the mechanical behavior of the specific lattice structures made of Ti6Al4V and provides insights for the development of mechanically optimized anatomically shaped lattice scaffolds.

This work is aimed at studying the tribological contact between a titanium–aluminum–vanadium alloy pin (Ti6Al4V), produced by Electron Beam Melting (EBM), and a sheet of ultra high molecular weight polyethylene (UHMWPE), which are widely utilized materials for prosthetic applications. Using a "pin on disc" system, tribometric tests of different duration (up to 240 min) were carried out in order to trace the trend of the polymer mass loss as a function of test time. In this way it was possible to identify the stationary phase of adhesive friction, at which the specific wear rate, which characterizes the tribological system under different lubrication conditions, was obtained. As for the pin, no weight losses were measured, while the optical observations on the tip showed a compressive effect after the entire test campaign. [ABSTRACT FROM AUTHOR]

Metal powder bed-based Additive Manufacturing (AM) technologies, such as Electron Beam-Melting (EBM) and Laser Powder Bed Fusion (LPBF), are established in several industries due to the large design freedom and mechanical properties. While EBM and LPBF have similar operating steps, process-specific characteristics influence the component design. The differences in the energy coupling lead to differing solidification conditions, microstructures, and, thus, mechanical properties. The surface finish and geometrical accuracy are also affected. As opposed to LPBF, EBM powder layers are preheated prior to selective melting. In this study, similar volume energy densities in LPBF and EBM were used to manufacture Ti6Al4V test geometries to assess the process transferability. Since the energy coupling of LPBF and EBM differ, heat source absorption was considered when calculating the volume energy density. Even when a similar volume energy density was used, significant differences in the component quality were found in this study due to specific respective process constraints. The extent of these constrains was investigated on voluminous samples and support-free overhanging structures. Overhang angles up to 90° were manufactured with LPBF and EBM, and characterized with regard to the relative density, surface roughness, and geometric compliance. [ABSTRACT FROM AUTHOR]

Innovative carbon reduction and sustainability solutions are needed to combat climate change. One promising approach towards cleaner air involves the utilization of lithium-ion batteries (LIB) and electric power vehicles, showcasing their potential as innovative tools for cleaner air. However, we must focus on the entire battery life cycle, starting with production. By prioritizing the efficiency and sustainability of lithium-ion battery manufacturing, we can take an essential step toward mitigating climate change and creating a healthier planet for future generations. A comprehensive case study of the leading LIB manufacturers demonstrates the usefulness of the suggested hybrid methodology. Initially, we utilized the Malmquist model to evaluate these firms’ total efficiency while dissecting their development into technical and technological efficiency change components. We employed the Epsilon-Based Measure (EBM) model to determine each organization’s efficiency and inefficiency scores. The findings show that the EBM approach successfully bridged the gap in the LIB industry landscape. Combined with the Malmquist model, the resulting framework offers a powerful and equitable evaluation paradigm that is easily applicable to any domain. Furthermore, it accurately identifies the top-performing organizations in specific aspects across the research period of 2018–2021. The EBM model demonstrates that most organizations have attained their top level, except for A10, which has superior technology adoption but poor management. A1, A2, A4, A6, A8, A9, and A10 were unable to meet their targets because of the COVID-19 pandemic, despite productivity improvements. A12 leads the three highest-scoring enterprises in efficiency and total productivity changes, while A3 and A5 should focus on innovative production techniques and improved management. The managerial implications provide vital direction for green energy practitioners, enhancing their operational effectiveness. Concurrently, consumers can identify the best LIB manufacturers, allowing them to invest in long-term green energy solutions confidently.

This study illustrates the utility and effectiveness of machine learning algorithms in identifying dependencies in data transmitted in industrial networks. The analysis was performed for two different algorithms. The study was carried out for the XGBoost (Extreme Gradient Boosting) algorithm based on a set of decision tree model classifiers, and the second algorithm tested was the EBM (Explainable Boosting Machines), which belongs to the class of Generalized Additive Models (GAM). Tests were conducted for several test scenarios. Simulated data from static equations were used, as were data from a simulator described by dynamic differential equations, and the final one used data from an actual physical laboratory bench connected via Modbus TCP/IP. Experimental results of both techniques are presented, thus demonstrating the effectiveness of the algorithms. The results show the strength of the algorithms studied, especially against static data. For dynamic data, the results are worse, but still at a level that allows using the researched methods to identify dependencies. The algorithms presented in this paper were used as a passive protection layer of a commercial IDS (Intrusion Detection System).

Background: Choosing an antipsychotic medication is an important medical decision in the treatment of schizophrenia. This decision requires risk-benefit assessments of antipsychotics, and thus, shared-decision making between physician and patients is strongly encouraged. Although the efficacy and side-effect profiles of antipsychotics are well-established, there is no clear framework for the communication of the evidence between physicians and patients. For this reason, we developed an evidence-based shared-decision making assistant (SDM-assistant) that presents high-quality evidence from network meta-analysis on the efficacy and side-effect profile of antipsychotics and can be used as a basis for shared-decision making between physicians and patients when selecting antipsychotic medications. Methods: The planned matched-pair cluster-randomised trial will be conducted in acute psychiatric wards (n = 14 wards planned) and will include adult inpatients with schizophrenia or schizophrenia-like disorders (N = 252 participants planned). On the intervention wards, patients and their treating physicians will use the SDM-assistant, whenever a decision on choosing an antipsychotic is warranted. On the control wards, antipsychotics will be chosen according to treatment-as-usual. The primary outcome will be patients' perceived involvement in the decision-making during the inpatient stay as measured with the SDM-Q-9. We will also assess therapeutic alliance, symptom severity, side-effects, treatment satisfaction, adherence, quality of life, functioning and rehospitalizations as secondary outcomes. Outcomes could be analysed at discharge and at follow-up after three months from discharge. The analysis will be conducted per-protocol using mixed-effects linear regression models for continuous outcomes and logistic regression models using generalised estimating equations for dichotomous outcomes. Barriers and facilitators in the implementation of the intervention will also be examined using a qualitative content analysis. Discussion: This is the first trial to examine a decision assistant specifically designed to facilitate shared-decision making for choosing antipsychotic medications, i.e., SDM-assistant, in acutely ill inpatients with schizophrenia. If the intervention can be successfully implemented, SDM-assistant could advance evidence-based medicine in schizophrenia by putting medical evidence on antipsychotics into the context of patient preferences and values. This could subsequently lead to a higher involvement of the patients in decision-making and better therapy decisions. Trial registration: German Clinical Trials Register (ID: DRKS00027316, registration date 26.01.2022). [ABSTRACT FROM AUTHOR]

Maritime transport, which includes shipping and port operations, is the fundamental basis of international trade and globalization. In transportation management, efficiency is critical for verifying performance and proposing the best countermeasure to meet predetermined goals. Various efforts in this field have been made to solve this problem satisfactorily. However, the significant proportion of conventional approaches are based on long-term observations and professional expertise, with only a few exceptions based on practice-based historical data. Data Envelopment Analysis (DEA) is a non-parametric technique for analyzing various output and input variables parallelly. The efficiency of maritime transport in European countries is explored using a two-stage DEA approach based on Malmquist and Epsilon-Based Measure (EBM). First, the Malmquist model analyses countries' total productivity growth rates and their breakdown into technical efficiency (catch-up) and technology change (frontier-shift). Second, the EBM model is used to determine the efficiency and inefficiency of the maritime transportation systems in each European country. Apart from identifying the best-performing countries in specific areas over the study period (2016–2019), the results highlight that the gap in applying the EBM method to maritime transport has been successfully closed and that the emerging paradigm, when combined with the Malmquist model, can be a sustainable and appropriate evaluation model for other research areas. [ABSTRACT FROM AUTHOR]

The three-report review was aimed at describing the historical development of clinical trials, controlled trials (CT) and randomized controlled trials (RCT), and the inclusion of these experimental approaches in disciplines related to both the health of individuals and populations (medicine and epidemiology). In Report 1, the authors consider the terminology issues applied to CT and RCT, the sources of the involved concepts, and relevant disciplines. It was shown that the terms ‘control’ and ‘trial’ appeared in experimental literature only at the end of the 19th century, ‘CT’ appeared in the first third or quarter of the 20th century, and the term ‘RCT’ appeared only in 2000s. It was found that approaches with CT and RCT were often included even in classical epidemiology, and this fact eliminates the specificity of differences between observational and experimental disciplines and blurred the difference between inductive and deductive methodologies. Scientific, philosophical, conceptual, and historical aspects were also considered for three areas that included CT and RCT: epidemiology, clinical epidemiology and evidence-based medicine (EBM). It was concluded that classical epidemiology, using predominantly inductive approaches, was not the scope of real medical experiments, in the first place, and, secondly, its prognosis was not aimed at the individual. At the same time, both clinical epidemiology and EBM, which mainly use deductive approaches, involve experiments, making it possible to make prognosis for a particular patient. The proposed summarizing scheme of the historical origins and philosophical foundations of disciplines aimed at finding and proving health effects using observational and experimental approaches reflects the problems considered and covers individual time milestones, which, as a rule, are not named in modern epidemiology textbooks and textbooks of other disciplines.

Metal lattice structures produced by means of additive techniques are attracting increasing attention thanks to the high structural efficiency they can offer. In order to achieve the maximum structural performance, numerical design techniques are used almost exclusively, thus based on CAE-FEM codes. Nevertheless, the current manufacturing facilities do not yet guarantee defect-free components, and, therefore, such imperfections need to be introduced in the numerical models too. The present work aims to describe a FE modelling technique for lattice structures based on the use of beam and shell elements, and therefore with a very reduced computational cost. The main structural parameters, such as weight and stiffness and strength, are used to drive the model calibration. Simple mathematical relationships, based on Experimental-CAD-FEM comparisons, are provided to estimate the error related to the numerical model in a simple and fast way. The validation was performed by three-point bending test on flat specimen with regular octet-truss microstructure both with and without external skin. The test articles were produced in Ti6Al4V and by means of the electron beam melting (EBM) technology. The results obtained are in excellent agreement with the experimental ones, indeed the maximum error is about 3%. All this indicates these methodologies as possible tools for evaluating the performance of such kinds of high-tech structures. [ABSTRACT FROM AUTHOR]

Abstract Background The importance of ensuring medical students are equipped with the skills to be able to practice evidence-based medicine (EBM) has been increasingly recognized in recent years. However, there is limited information on an effective EBM curriculum for undergraduate medical schools. This study aims to test the feasibility of integrating a multifaceted EBM curriculum in the early years of an undergraduate medical school. This was subsequently evaluated using the validated Fresno test and students’ self-reported knowledge and attitudes as they progressed through the curriculum. Methods EBM was integrated horizontally and vertically into the curriculum into the first 2 years of undergraduate medical school. First year medical students were recruited to participate in the study. The 212-point Fresno test was administered along with a locally developed questionnaire at baseline before EBM teaching in year one and at the end of EBM teaching in year two. Results Thirty-one students participated at baseline and 55 students participated at the end of second year EBM teaching. For the 18 students who completed the Fresno at both time points, the average score increased by 38.7 marks (p

Abstract Background Evidence-based medicine (EBM) is a core competence in both undergraduate and postgraduate medical curricula. However, its integration into curricula varies widely. Our study will help medical colleges develop, implement and evaluate their EBM courses. We assessed the effectiveness of workshops in improving critical appraisal skills among medical students. Methods A before-and-after study design without a control group was used. A 5-week short EBM module including lectures, workshops, and online search sessions was conducted with 52 fourth-year medical students during their primary healthcare course at the College of Medicine, Princess Nourah bint Abdulrahman University. Statistical analysis was performed using SPSS statistical software (version 20, SPSS Inc., Chicago, US). Parametric tests as well as Student’s paired t-test for pre- and post-test comparisons were used. Results Forty-nine (49) participants completed the pre- and post-training Fresno tests, and 44.9% of the participants had a GPA of 4.0 or higher. The mean Fresno test score increased from 45.63 (SD 21.89) on the pre-test to 64.49 (SD 33.31) on the post-test, with significant improvements in the following items: search strategies, relevance, internal validity, magnitude and significance of results, statistical values of diagnosis studies (sensitivity, specificity, and LR), statistical values of therapy studies (ARR, RRR, and NNT), and best study design for diagnosis and prognosis (P

The aim of this article was to estimate the total efficiency of teaching activity carried out in Polish higher education institutions in 2015. The study used the EBM model belonging to the nonparametric DEA method. The output of the education process was measured by the number of graduates of the first-cycle and second-cycle studies and long-cycle studies, the number of doctoral degrees awarded, the number of postgraduate studies certificates issued, and the number of academic teachers and the total value of teaching activity revenue, the total value of state budget subsidies along with fees for educational services and fixed assets. Efficiency measurement was made for two variants: M1 and M2. The obtained research results indicate that on average, the difference between the indicators of the total efficiency of teaching activity under the two empirical models is 0.00. This is due to the fact that the share of budget subsidies and fees paid by students of part-time studies, in the revenues from teaching activity is at the level of 87%. There is also a positive but weak correlation between the total efficiency ratios and financial results obtained from teaching activities.

Debris formation is a crucial aspect that determines the lifespan of prosthetic joints. The wearing contact between ultra-high molecular weight polyethylene (UHMWPE) and a Ti alloy surface has been studied in the literature. However, when measuring mass loss, potential errors can arise due to the very small values involved (on the order of some units of 0.1 mg in experiments lasting several hours) and be caused by the absorption of humidity in the specimen, in addition to the lack of accuracy typical of weight scales. These errors can hardly be avoided, but accurate cleaning and drying processes can minimize them. With these premises, the present work aims to determine, by pin-on-disc test, the wear effect in the UHMWPE rotating sheet and Ti6Al4V pin produced by Electron Beam Melting (EBM) under dry and lubricated conditions. The morphology of the worn surface was documented by optical microscopy, and the volume loss of both the rotating specimens and the pin was accurately calculated through the detection of the wear track observed by optical microscopy. In particular, the present work proposes a method for directly determining the volume loss of the polymer to compare it with that obtained with the weight measurement. For both procedures, the uncertainty in evaluating the specific wear rate was analyzed, demonstrating that volume measurement allows for avoiding any possible error associated with weighing the polymeric specimens.

The journey of receiving blood as a patient with transfusion-dependent beta thalassemia has profoundly shaped my understanding of the life-saving power of blood donation. This personal experience underscores the critical importance of blood donors, not just for individual recipients but for the broader community, enhancing public health, productivity, and well-being. There are several challenges to securing a blood donor pool in current health care climate. Solutions that focus on the engagement of donors, clinicians, and patients are key to improving the donor pool and utilizing the blood supply in a judicious manner., (©Ankita Sagar. Originally published in JMIR Perioperative Medicine (http://periop.jmir.org), 27.09.2024.)

China is the world's largest electricity producer and consumer, and the healthy development of the electricity industry contributes to its economic growth and social stability. Yangtze River Economic Belt (YREB) and non-YREB are two major economic engines of great strategic significance for China with distinct differences in the external development environment of their power industry. A cross-regional comparison of electricity generation efficiency in China is meaningful to obtain a more accurate understanding of the power generation efficiency of YREB and non-YREB, but there is currently no literature in this area as well as none for comparing electricity generation efficiency between different energy sources. Therefore, this research uses the meta-frontier epsilon-based measure (EBM) data envelopment analysis (DEA) model to calculate the efficiency of different electricity generation energy in YREB and non-YREB for the period 2013–2017. The empirical results show that: 1) YREB's electricity industry has higher electricity generation efficiency than non-YREB's, and the efficiency gap is continuing to widen; 2) YREB's average thermal electricity generation efficiency is higher than non-YREB's; 3) YREB's clean energy power generation efficiency is always higher than non-YREB's; 4) regional differences in YREB are smaller than those in non-YREB; and 5) the efficiency value of clean energy power generation is lower than that of thermal power generation. From our analysis results, this paper presents some policy recommendations.