High Level Of Efficiency Research Articles

Technology Focus: Artificial Lift (March 2024)

The industry is now in its second year beyond the COVID-19 pandemic, and the workforce has changed in many ways, but issues facing production performance remain. Major conferences saw 116 submissions from 28 countries, with 64% regarding electrical submersible pumps (ESPs), and 18% and 8% devoted to gas lift and rod lift, respectively. Many of the papers dealt with case history successes. That is all well and good, but it doesn’t necessarily drive reader interest if it can’t be applied to their asset. The top three papers were selected on the guidelines of clarity of the abstract in addressing the scope, methods/procedures/process, results/observations/conclusions, and novel or additive information. The papers described here focus on novel unconventional horizontal downhole pump card signatures, carbon reduction through alternative energy to pump wells, and predicting downhole-discharge pressure of ESPs. Paper SPE 214723 presents a discussion of the design, evaluation, and field testing of a new type of mechanical gas separator with what the authors term a “hydrohelical” design. The separator, in Permian Basin tests, almost doubled the flow capacity of conventional gas separators with a high level of efficiency. The authors draw particular attention to the improvement of ESP operations under multiphase conditions. Carbon reduction is a hot topic, and paper OTC 32671 does not disappoint as it looks at the use of a 10-MW floating solar photovoltaic power system to meet the energy needed for 10 ESP wells on an oil platform in the Brazil equatorial region. Collaboration with the National Renewable Energy Laboratory and its System Advisor Model was used with extensive meteorological and wind data to examine the sustainability of green energy use for the petroleum industry and offer valuable guidance for future renewable energy projects in regions with similar characteristics. Finally, paper SPE 216598 comes from Sudan and Saudia Arabia and focuses on digital solutions for the challenge of knowing the downhole-discharge pressure of an ESP system. Most ESP systems do not have downhole gauges, so an artificial neural network (ANN) approach is used to predict the ESP discharge pressure for performance optimization. Forty wells and more than 12,000 data points were reduced to 16 inputs plus one hidden layer of seven neurons to provide a high degree of accuracy in prediction of discharge pressure. The authors state that the ability to accurately predict discharge pressure can lead to the early detection of possible anomalies, which can prevent costly failures and reduce downtime. Artificial intelligence technology may improve the accuracy of ANNs, but, for now, ANNs are worth a look to obtain much-needed ESP discharge pressure. Other notable papers dealt with gas separation, ESP reliability, and the increasing challenges of low rates, low pressures, and high gas fractions. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214913 Solved: ESP Shutdowns and Restart Issues—Novel Pump Protection and Active 3D Dispersion System Improve ESP Performance in Multiple Wells by A. Bhattacharjee, SLB, et al. SPE 215474 A New Artificial Lift Approach in Telisa Low-Productivity Reservoir: Utilized Very Low-Rate ESP by Agus Aryanto, Medco E&P Indonesia, et al. SPE 215170 A Comprehensive Study of a Novel Submersible Pump by Hattan Banjar, Saudi Aramco, et al. SPE 214722 Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence and Physics-Based Hybrid Models by Shejuti Silvia, Baker Hughes, et al. SPE 214733 Gas-Flow-Management Technology Designed To Decrease Downtime and Improve ESP Efficiency by S. Fulwider, ConocoPhillips, et al.

A multi‐objective optimization approach for beam pattern synthesis ofUAVvirtual rectangular antenna array

AbstractVirtual antenna array (VAA) formed by unmanned aerial vehicle (UAV) antenna units using collaborative beamforming (CB) technology plays an important role in the air communication system, and can be used in radar, military, disaster rescue and other places. However, there are still some issues with the beam pattern formed by this method, such as high sidelobe level (SLL), high cost and low efficiency. In this article, each UAV carries an omnidirectional antenna unit, and a large number of UAVs form a UAV virtual rectangular antenna array (UVRAA) to communicate with the ground base station (BS). We formulate an overhead minimization and efficient communication multi‐objective optimization problem (OMECMOP) which jointly optimize the excitation current weights of the UVRAA and reduce the number of UAVs in operation to improve the beam pattern, enhance the communication efficiency and decrease the overhead of UVRAA. In addition, we also propose an improved multi‐objective multi‐verse optimization algorithm based on the inverse decline curve type (ISDT‐MOMVO) which introduces a strategy optimization initialization solution with quasi‐opposition based learning (QBL) and a hybrid solution updating operators to solve the OMECMOP. The simulation results show that compared with other traditional swarm intelligence (SI) optimization algorithms the ISDT‐MOMVO algorithm produces better beam pattern and the thinning rate can reach 50%.

EFISIENSI TEKNIS PRODUKSI TANAMAN JAHE DI PROVINSI JAWA TIMUR: PENDEKATAN STOCHASTIC FRONTIER ANALYSIS

<em>Ginger is a spice and medicinal plant that much needed by industry and society. However, ginger production decreased and even experienced a trade balance deficit. One solution that can be done is through intensification or efficiency improvement, especially in the largest ginger-producing province in Indonesia, namely East Java Province. The data used is raw data of Cost Structure of Horticultural Cultivation Household Survey (SOUH) 2018. The results of the analysis using Stochastic Frontier Analysis (SFA) show that four factors of production, namely land area, number of workers, amount of seeds used, and amount of fertilizer used, can increase ginger production significantly with a fairly high level of technical efficiency. However, technical inefficiencies are still found which cause ginger production to not be maximized. There are seven from eight variables that have a significant effect, namely farmer age, farmer education level, farmer group participation, pest control, receiving farm assistance, use of credit, and land ownership status. Meanwhile, the variable of farmer gender has no significant effect.</em>

Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging.

Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour. To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data. Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared. For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI. The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

Building Information Modeling and Geographic Information System: Integrated Framework in Support of Facility Management (FM)

Currently, the management of public real estate stocks is one of the most challenging activities. In this case, facility management emerges as a strategic but complex discipline that requires a great amount of integrated and structured data and information of heterogeneous origin and nature. Starting from this premise, this paper focuses on how FM discipline can be supported by Building Information Modeling (BIM) and Geographic Information System (GIS) integrated methodologies and tools achieving high levels of effectiveness and efficiency in public building stock management. This led to the definition and development of a framework that mainly takes advantage of open-source tools (e.g., Quantum Geographic Information System—QGIS, and Dynamo) and non-proprietary data exchange formats like the COBie (Construction Operations Building Information Exchange) international standard. This methodological framework is capable of integrating different but strongly complementary tools, data, information, and expert knowledge for a multiscale approach. The methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform but leads instead to an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. The findings of experimental applications of the methodological approach to the former Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are presented and discussed. Finally, the analysis about potentials and criticalities of the developed methodological framework aims to trigger the widest future development and dissemination of this research.

Strawberry Maturity Recognition Based on Improved YOLOv5

Strawberry maturity detection plays an essential role in modern strawberry yield estimation and robot-assisted picking and sorting. Due to the small size and complex growth environment of strawberries, there are still problems with existing recognition systems’ accuracy and maturity classifications. This article proposes a strawberry maturity recognition algorithm based on an improved YOLOv5s model named YOLOv5s-BiCE. This algorithm model is a replacement of the upsampling algorithm with a CARAFE module structure. It is an improvement on the previous model in terms of its content-aware processing; it also widens the field of vision and maintains a high level of efficiency, resulting in improved object detection capabilities. This article also introduces a double attention mechanism named Biformed for small-target detection, optimizing computing allocation, and enhancing content perception flexibility. Via multi-scale feature fusion, we utilized double attention mechanisms to reduce the number of redundant computations. Additionally, the Focal_EIOU optimization method was introduced to improve its accuracy and address issues related to uneven sample classification in the loss function. The YOLOv5s-BiCE algorithm was better at recognizing strawberry maturity compared to the original YOLOv5s model. It achieved a 2.8% increase in the mean average precision and a 7.4% increase in accuracy for the strawberry maturity dataset. The improved algorithm outperformed other networks, like YOLOv4-tiny, YOLOv4-lite-e, YOLOv4-lite-s, YOLOv7, and Fast RCNN, with recognition accuracy improvements of 3.3%, 4.7%, 4.2%, 1.5%, and 2.2%, respectively. In addition, we developed a corresponding detection app and combined the algorithm with DeepSort to apply it to patrol robots. It was found that the detection algorithm exhibits a fast real-time detection speed, can support intelligent estimations of strawberry yield, and can assist picking robots.

Coordinated and uncoordinated punishment in a team investment game

Coordinated punishment occurs when punishment requires a specific number of punishers to be effective, otherwise, no damage will be inflicted on the target. While societies often rely on this punishment device, its benefits are unclear compared to uncoordinated punishment, where punishment decisions are substitutes. In this paper, we compare the efficacy of coordinated and uncoordinated punishment in a team investment game with two investors and one allocator. Our findings indicate that coordinated punishment results in higher levels of cooperation and reciprocity, as measured by the levels of joint investment and the return by allocators. Importantly, this does not translate into higher payoffs: investors use punishment more frequently when this is coordinated, which destroys the efficiency gains generated by the highest investment. In fact, our results suggest that the highest level of efficiency would be achieved if investors were not allowed to punish.

Uptake pathways of cell-penetrating peptides in the context of drug delivery, gene therapy, and vaccine development

Cell-penetrating peptides have been extensively utilized for the purpose of facilitating the intracellular delivery of cargo that is impermeable to the cell membrane. The researchers have exhibited proficient delivery capabilities for oligonucleotides, thereby establishing cell-penetrating peptides as a potent instrument in the field of gene therapy. Furthermore, they have demonstrated a high level of efficiency in delivering several additional payloads. Cell penetrating peptides (CPPs) possess the capability to efficiently transport therapeutic molecules to specific cells, hence offering potential remedies for many illnesses. Hence, their utilization is imperative for the improvement of therapeutic vaccines. In contemporary studies, a plethora of cell-penetrating peptides have been unveiled, each characterized by its own distinct structural attributes and associated mechanisms. Although it is widely acknowledged that there are multiple pathways through which particles might be internalized, a comprehensive understanding of the specific mechanisms by which these particles enter cells has to be fully elucidated. The absorption of cell-penetrating peptides can occur through either direct translocation or endocytosis. However, it is worth noting that categories of cell-penetrating peptides are not commonly linked to specific entrance mechanisms. Furthermore, research has demonstrated that cell-penetrating peptides (CPPs) possess the capacity to enhance antigen uptake by cells and facilitate the traversal of various biological barriers. The primary objective of this work is to examine the mechanisms by which cell-penetrating peptides are internalized by cells and their significance in facilitating the administration of drugs, particularly in the context of gene therapy and vaccine development. The current study investigates the immunostimulatory properties of numerous vaccine components administered using different cell-penetrating peptides (CPPs). This study encompassed a comprehensive discussion on various topics, including the uptake pathways and mechanisms of cell-penetrating peptides (CPPs), the utilization of CPPs as innovative vectors for gene therapy, the role of CPPs in vaccine development, and the potential of CPPs for antigen delivery in the context of vaccine development.

Constructing blocked-nanolayer by surface charge inversion over anion exchange membrane for improved antifouling performance

Anion exchange membranes (AEMs) with excellent antifouling property are essential for treating highly saline organic wastewater through electrodialysis (ED). In this study, we proposed a strategy of surface charge inversion to construct a blocked- nanolayer based on in-suit dopamine (DA) deposition followed by grafting negative-charged 2-acrylamide-2-methylpropanesulfonic acid (AMPS) for promoting the antifouling performance of AEM. It's indicated that the surface hydrophilicity and negative charge density was gradually enhanced with the increase of AMPS grafting density without sacrifices of ion exchange capability, swelling ratio and desalination performance. In the simulated fouling experiments, the optimal AEM-AMPS-105 membrane exhibited excellent antifouling property due to the improved surface hydrophilicity and high electrostatic repulsion against organic matter. It's highlighted that the transmembrane voltage of modified membrane after operating 200 min was reduced from 7.12 V to 4.85 V with a significant decrease of energy consumption by 55.1 % while maintaining a high current efficiency level of 85.5 %. Moreover, the modified membrane performed significantly enhanced salt/contaminant selectivity and high stability in dye wastewater treatment. We anticipated that the construction of charge-blocked nanolayer by surface charge inversion offered a practical method for improving the organic fouling-resistance of AEMs.

The Impact of Changing Collaborative Workplace Parameters on Assembly Operation Efficiency

Human–robot collaborative systems bring several benefits in using human and robot capabilities simultaneously. One of the critical questions is the impact of these systems on production process efficiency. The search for high-level efficiency is severely dependent on collaborative robot characteristics and motion parameters, and the ability of humans to adjust to changing circumstances. Therefore, our research analyzes the effect of the changing collaborative robot motion parameters, acoustic parameters and visual factors in a specific assembly operation, where efficiency is measured through operation times. To conduct our study, we designed a digital twin-based model and a laboratory environment experiment in the form of a collaborative workplace. The results show that changing the motion, acoustic and visual parameters of the collaborative workplace impact the assembly process efficiency significantly.

Photosynthetic potential of Malus domestica columnar group

Productivity is one of the primary economic and biological characteristics of an apple tree variety; it is this characteristic that determines the value and economic feasibility of the variety. The photosynthetic activity of the leaf surface of plants plays a leading role in forming the potential productivity of the apple tree. By "potential productivity," we mean the productivity of plants under conditions of ideal agroenvironment, optimal nutrition, and absence of diseases. To diagnose the potential productivity of cultivars we used an indicator of chlorophyll fluorescence induction–induction coefficient (Кі). According to our data, the Кі of leaves of columnar cultivars ranged from 0.720 to 0.740, indicating a high level of efficiency of photophysical processes near the photosystem II (PS II) reaction centers. For plants of columnar cultivars, the viability index ranged from 1.78 to 2.19. It has been established that individual age sections of tree trunks form different productivity potentials. Thus, based on the chlorophyll a (Chl a) fluorescence induction index, a higher intensity of photosynthesis was observed in the leaves of the cultivars ‘Valuta’, ‘Favoryt’, and ‘Bilosnizhka’ on seven to nine-year-old trunks. In contrast, for ‘Tantsivnytsia’, the highest intensity was observed on the oldest spur formation (14–19 years old). In traditional apple cultivars, the intensity of photosynthesis, as indicated by Кі, decreased with the age of spur formation; however, such a decline was not observed in columnar cultivars. The leaves of plants from the traditional cultivar ‘Idared’, situated on a homogeneous shoot, exhibited the highest photosynthetic intensity based on the F680γt / F680βt light intensity. Conversely, in the leaves of three-year-old spur formations, a 25.3% decrease in photosynthetic intensity was observed, falling to a 45.7% decrease in six-year-olds. With the optimal combination of agroecological factors for columnar cultivars, stability in the photosynthetic potential across various complex spur formations is observed. In typical apple cultivars, the age of spur formation leads to a suppression of photosynthetic intensity. The cultivar ‘Bilosnizhka’ is characterized by the highest Chl a/ Chl b content, indicating lower adaptability. In the leaves of this cultivar, the amount of Chl a exceeded Chl b by three times; in contrast, in the cultivars ‘Sparta’ and ‘Tantsivnytsia’, the difference was 2.5 times higher. This value (2.5x) correlates with increased adaptability to the conditions in which they were studied. The highest level of leaf net productivity of photosynthesis (NPP) is observed in plants of the ‘Valuta’ cultivar (13.9 g/m² for day); in the cultivars ‘Bilosnizhka’, ‘Favoryt’, ‘Bolero’, ‘Sparta’, ‘Tantsivnytsia’, and ‘Papirovka’, the accumulation of dry matter is lowered by 32.6–40.6%.

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.

Evaluation Model of Human Resource Allocation Balance for Healthy Environment in Organizations Based on Big Data Driven and Internet of Things

Abstract To improve the ability of balanced evaluation, dynamic allocation, level of output, and profitability of human resources, a balanced evaluation model of human resources allocation is proposed based on big data driven and the Internet of Things. An evaluation architecture model of human resource allocation balance in view of big data driven and the Internet of Things is established; a multithread big data driven configuration model and the Internet of Things are used to construct a table model of enterprise human resource optimal allocation in the form of reports; statistical regression analysis method is used to detect the risks and process parameters in the process of enterprise human resource optimal allocation; and employment elasticity theory analysis is taken to establish a resource factor analysis model of human resource balanced allocation under the constraint of economic growth mode. According to the changing trend of the scale and structure of labor resources, statistical regression analysis is adopted to make big data driven analysis in the process of balanced evaluation of human resources allocation, and balanced game control is adopted to analyze the relevant factors affecting human resources allocation to realize balanced scheduling of population flow and human resources allocation and automatic post adjustment. The empirical analysis results show that the system has a good balance in human resource allocation, the output efficiency among the various elements of human resource allocation is improved, and there is a high level of efficiency in human resource output.

Investigation of physicochemical characterization and magnetic enrichment of iron ore from Sidi Maarouf deposit

The establishment of a new metallurgical complex in Bellara, located in El-Milia in the Jijel region, is geographically advantageous due to its proximity to the Sidi Maarouf deposit (Algeria). This proximity has been a fundamental motivation for the completion of the current study. The object of research is the quality of iron products from Sidi Maarouf. The research is aimed at developing a treatment process aimed at improving the quality of iron products from Sidi Maarouf, while reducing the impurities of quartz and clay present in this ore. This approach will be based on an approved technological treatment scheme, implemented through an experimental approach to ensure reliable results that meet the requirements of the steel industry. The problem issue revolves around the difficulties encountered in the production of steels due to the natural characteristics of the raw material. In the absence of a physico-chemical characterization of the Sidi Maarouf iron ore deposit, samples taken undergo a series of thorough analyses, including microscopic examinations, X-ray diffraction (XRD), as well as additional chemical analyses using X-ray fluorescence (XRF). The identified minerals are predominantly composed of hematite in terms of iron. As for the gangue, it is mainly composed of calcite and quartz. Through to the pre-treatment process involving washing, we have successfully removed lightweight particles, resulting in a concentrate containing dense particles. This approach was crucial in achieving satisfactory results during the high-intensity dry magnetic separation. As a result of the research it is shown that the enrichment of the Sidi Maarouf iron ore through high-intensity dry magnetic separation, a final concentrate was obtained with a Fe2O3 content exceeding 67 %, along with a significant reduction in silica impurities to 0.92 % and alumina to 0.77 %. It was concluded that this concentrate, derived from the -1+0.5 mm fraction and obtained under a current intensity of 12 A, meets the requirements of the steel industry. Following the work carried out, it is found that the methods of valorization of iron ore through mineralogical processes consistently require a high level of efficiency and performance in terms of equipment and characterization of the products obtained. In the future, iron ore processing will be conducted using innovative methods, integrating advanced technologies to enhance its characteristics while adopting environmentally friendly practices.

Development of an optical sensing system for the detection of lubricity conditions on the rail gage face

ABSTRACT A novel optical sensing device that uses laser-induced fluorescence to detect the lubricity condition of the rail gage-face is evaluated. By analysing the fluorescence properties of eight commonly used gage-face greases used by the U.S. Class I railroads, we observed that the emitted fluorescence of the grease when exposed to ultraviolet (UV) light of different wavelengths can be used for detecting their presence. This observation is used to develop a non-contacting optical sensing device that uses two 405 nm lasers, a fluorescence detector, and a fluorescent bandpass filter, which are optimized for the fluorescence spectra of the greases. Two sensing devices are installed on a robotic rail cart, one on each side, to simultaneously detect the lubricity conditions on the rails over extended distances. After extensive evaluations using both laboratory and field testing, it is confirmed that the optical sensing system can accurately detect the presence of the gage-face grease through the fluorescence intensity or intensity variance, even when there are small quantities of grease in relatively thin layers. Furthermore, the test results indicate a high level of repeatability and efficiency in detecting the spatial distribution of grease along the rail.

Design and analysis of fault-tolerant sequential logic circuits for safety-critical applications

Safety-critical systems used in applications that demand high levels of dependability, efficiency, and fault-tolerance often use sequential logic circuits in its design and implementation. The safety-critical digital system typically uses latches, flip-flops, and other memory elements, which are prone to the effects of natural faults and single event upsets (SEUs) caused by radiation-induced effects. The faults can lead to subsystem failures due to the continuous advancement in the realization of the small size transistor. To design a reliable digital-based system, it is essential to develop new fault-tolerance approaches that are integrated into the design of sequential logic circuits. This work proposes a novel fault-tolerant approach based on the redundancy of sequential logic circuit, which consists of a variety of design components, D flip-flop storage elements linked to a fault injection unit, a duplicate modular redundancy, and data monitoring units with a switching circuit. The experimental simulation results using a five-state Markov chain analysis model prove that the proposed fault-tolerant system can achieve 0.99999998 for reliability of the fault detection coverage (C) which equal to 0.99999. Finally, we believe that using this new approach of fault-tolerance and redundancy would improve the dependability and reliability of next generation safety-critical applications.

Determination and classification of entrepreneurial efficiency of countries: Data envelopment analysis and hierarchical clustering analysis

Entrepreneurship initiatives have undeniable effects on national economies. States and governments produce various strategies and policies to increase the contribution of entrepreneurship to the country's economy. Entrepreneurship levels of countries are determined by various organizations. Country entrepreneurship scores and rankings are regularly published by the “Global entrepreneurship monitor (GEM)”. Countries, on the other hand, understand their current level of entrepreneurship according to these reports. In this research, it is aimed to determine the entrepreneurial activity efficiency levels (EAE) of the countries with the data obtained from the GEM 2021 reports and to cluster them according to their activity levels. In this context, forty-two country data of five indicators presented in the 2021 GEM report were used. The research was carried out in two stages. In the first stage, four output-oriented data envelopment (DEA) models were created and the EAE were determined. In the second stage, the clustering of countries according to their EAEs was carried out by hierarchical clustering analysis. According to the research findings, 21 countries were at full efficiency in the DEA-1 model, 22 countries were at full efficiency in the DEA-2 model, and 18 countries were at full efficiency in the DEA-3 and DEA-4 models. In the hierarchical clustering analysis, the countries are clustered in three groups. Twenty-two countries were included in Cluster-1, seven countries in Cluster-2, and thirteen countries in Cluster-3. Cluster-1, Cluster-2 and Cluster-3 were characterized as high, middle, and low efficiency levels, respectively. As a result of the research, suggestions were made to countries to improve their entrepreneurial activities.

Модель психолого-педагогического сопровождения становления профессиональной идентичности у будущих педагогов профессионального образования

Realization of professional activity at a high level of efficiency requires from future teachers the formed qualita-tive filling of the position of a specialist, vision of themselves in the role of a teacher, correlation of actual train-ing and real requirements of professional-pedagogical activity and community, otherwise professional identity, as a means of formation of which is a psychological and pedagogical support. The aim of the study is to pre-sent a model of psycho-pedagogical support for the formation of professional identity in future teachers of vo-cational education. The work uses methods of qualitative theoretical analysis, comparison, classification, mod-eling. The model of psychological and pedagogical support based on systemic, activity, personality-oriented and reflexive approaches is presented. The stages of psycho-pedagogical support of students’ professional identity include the formation of: professional and pedagogical orientation of personality, creative readiness for professional activity, professional identity as a result of vocational training.

Materials in Solar Photovoltaic Technology: Advances, Challenges, and Environmental Considerations

Solar photovoltaic technology has experienced significant growth and development in recent years, making it a significant figure in the field of renewable energy. The basic principle of solar PV technology involves the conversion of sunlight into electricity using semiconductor materials. Silicon has consistently been the predominant material used in solar PV cells, but there is ongoing research and development into alternative materials. The choice of material for solar PV cells is crucial as it directly impacts the efficiency, cost, and environmental impact of the technology. Silicon-based solar cells have achieved high levels of efficiency and reliability, but they can be expensive to produce. On the other hand, emerging materials such as perovskites show great promise in terms of cost-effectiveness and efficiency, but they still face challenges related to stability and scalability. In addition to the semiconductor material, other components such as conductive metals, transparent conductive oxides, and encapsulation materials also play a crucial role in the performance and longevity of solar PV systems. Understanding the material properties, fabrication processes, and environmental impact of solar PV materials is essential for making informed decisions in the design and implementation of solar PV systems.

Input inefficiencies in the hotel industry. A non-radial directional performance measurement

This paper investigates the inefficiencies of inputs in the hotel industry. To do this, we apply the weighted Russell directional distance model (Barros et al., 2012; Chen et al., 2014) considering an input-oriented approach. This model allows to obtain not only aggregate inefficiency measures, but also their components. In a second stage, we analyze the individual and overall efficiency measures considering several factors in a truncated Bayesian Tobit regression. The empirical analysis is based on the hotel sector of two mature Spanish destinations (the Canary and Balearic Islands). Results show that although hotels located in both regions obtain high efficiency levels, they are notably inefficient in terms of capital costs management. Furthermore, overall results show that Canarian hotels are less efficient than Balearic hotels in the management of all inputs. Finally, the type of chain and location variables appear to affect the overall and individual efficiency scores obtained by Canarian hotels.