bluuebunny/crossref_metadata_2025 huggingface dataset:
D2BGAN: Dual Discriminator Bayesian Generative Adversarial Network for Deformable MR–Ultrasound Registration Applied to Brain Shift Compensation
Authors: Rahmani Mahdiyeh , Moghaddasi Hadis , Pour-Rashidi Ahmad , Ahmadian Alireza , Najafzadeh Ebrahim , Farnia Parastoo
Published: June 2024

During neurosurgical procedures, the neuro-navigation system’s accuracy is affected by the brain shift phenomenon. One popular strategy is to compensate for brain shift using intraoperative ultrasound (iUS) registration with pre-operative magnetic resonance (MR) scans. This requires a satisfactory multimodal image registration method, which is challenging due to the low image quality of ultrasound and the unpredictable nature of brain deformation during surgery. In this paper, we propose an automatic unsupervised end-to-end MR–iUS registration approach named the Dual Discriminator Bayesian Generative Adversarial Network (D2BGAN). The proposed network consists of two discriminators and a generator optimized by a Bayesian loss function to improve the functionality of the generator, and we add a mutual information loss function to the discriminator for similarity measurements. Extensive validation was performed on the RESECT and BITE datasets, where the mean target registration error (mTRE) of MR–iUS registration using D2BGAN was determined to be 0.75 ± 0.3 mm. The D2BGAN illustrated a clear advantage by achieving an 85% improvement in the mTRE over the initial error. Moreover, the results confirmed that the proposed Bayesian loss function, rather than the typical loss function, improved the accuracy of MR–iUS registration by 23%. The improvement in registration accuracy was further enhanced by the preservation of the intensity and anatomical information of the input images.
Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery
Authors: Berhouet Julien , Samargandi Ramy
Published: June 2024

In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These “markerless” systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.
Complete Blood Counts and Research Parameters in the Detection of Myelodysplastic Syndromes
Authors: Urrechaga Eloísa , Fernández Mónica , Aguirre Urko
Published: June 2024

The diagnosis of Myelodysplastic syndromes (MDS) is frequently challenging, especially in terms of the distinction from the other non-neoplastic causes of cytopenia. Currently, it is based on the presence of peripheral blood cytopenias, peripheral blood and bone marrow dysplasia/blasts, and clonal cytogenetic abnormalities, but MDS diagnostic features are polymorphic and non-specific. We investigated the utility of complete blood count (CBC) and research parameters (RUO) from the analyzer BC 6800 Plus (Mindray Diagnostics) to discriminate MDS-related cytopenia. Methods: 100 samples from healthy individuals were used to establish the values of research parameters in normal subjects. A retrospective study was conducted including 66 patients diagnosed with MDS, 90 cytopenic patients due to other diseases (cancer patients receiving therapy, aplastic anemia, other hematological malignancies) and 50 with macrocytic anemia. The Wilcoxon test was applied to detect statistical differences among the groups of patients, considering p < 0.05 significant. The diagnostic performance of the RUO parameters for discriminating MDS among cytopenias was evaluated using receiver operating characteristic (ROC) curve analysis. Amultivariable logistic regression model was performed to identify the potential predictors for having MDS. The area under curve (AUC) and the Hosmer–Lemeshow test of the model were assessed. The performance of the model was verified in a prospective study including 224 cytopenic patients (validation group). Results: In the MDS group, the mean cell volume (MCV), percentage of macrocytic red cells (MAC), red cell distribution width (RDW) and immature platelets fraction (IPF) had increased values compared to the cytopenic and normal patients, while platelets, red and white cell counts, Neu X (related to the cytoplasmic complexity of neutrophils), Neu Y (related to nucleic acid content) and Neu Z (related to cell size) were lower (p < 0.001). Neu X, Neu Y, and Neu Z showed higher AUC for detecting MDS > 0.80; MAC, RDW and IPF AUC > 0.76. The multivariable model demonstrated that Neu X and Neu Y could be used in the recognition of MDS, AUC 0.88. In the validation group, 89.0% of the MDS patients were well classified. Conclusion: MDS are common malignant disorders with a poor prognosis, and early diagnosis is warranted for optimal benefit from treatment. RUO gain insights to detect dysplasia of MDS and could be used in the differential diagnosis of MDS from cytopenias of other etiologies.
Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models
Authors: Pinton Philippe
Published: June 2024

Machine learning (ML) has been applied to predict the efficacy of biologic agents in ulcerative colitis (UC). ML can offer precision, personalization, efficiency, and automation. Moreover, it can improve decision support in predicting clinical outcomes. However, it faces challenges related to data quality and quantity, overfitting, generalization, and interpretability. This paper comments on two recent ML models that predict the efficacy of vedolizumab and ustekinumab in UC. Models that consider multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data are required for optimal shared decision-making and precision medicine. This paper also highlights the potential of combining ML with computational models to enhance clinical outcomes and personalized healthcare. Key Insights: (1) ML offers precision, personalization, efficiency, and decision support for predicting the efficacy of biologic agents in UC. (2) Challenging aspects in ML prediction include data quality, overfitting, and interpretability. (3) Multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data should be considered in predictive models for optimal decision-making. (4) Combining ML with computational models may improve clinical outcomes and personalized healthcare.
Disability Tax in the Welfare State: Uncertainty and Resentment about Disability Services in Finland
Authors: Katsui Hisayo
Published: June 2024

This paper focuses on the uncertainty and resentment that many persons with disabilities feel concerning their disability services in the welfare state of Finland. This paper elaborates on the theme through the lived citizenship of persons with disabilities using the key theory of disability tax as an analytical tool. The empirical data were collected through an online survey (n = 541) and six group interviews (n = 41) of persons with disabilities in 2023. The disability tax experiences are elaborated through four aspects: (1) rejected applications, (2) uncertain realisation, (3) laborious complaint mechanisms, and (4) the psycho–emotional effect. The findings of this study establish collective experiences of multi-layered disability tax throughout the disability services process. It concludes that disability services, which were originally planned to specifically ensure equal opportunities to participate in society for persons with disabilities, are increasingly becoming the very sources of burden as austerity has silently grown deeper over recent years and has become the clear policy of the current government.
A New Class of Separable Lagrangian Systems Generalizing Sawada–Kotera System
Authors: Gorni Gianluca , Scomparin Mattia , Zampieri Gaetano
Published: June 2024

Some characteristics of stationary flows of the Sawada–Kotera system lend themselves to generalization, producing a large class of separable Lagrangian systems with two degrees of freedom. All of these systems come in couples that have the same equations of motion, although they are not related by a gauge transform. Some nonpolynomial examples are provided.
Employing the Performance Evaluation Method to Examine the Efficacy of English Language Instruction: Insights from Primary Educational Institutions in Taiwan
Authors: Chiou Kuo-Ching , Huang Yu-Ming , Silalahi Andri Dayarana K.
Published: June 2024

The investigation examines English proficiency levels across Taiwanese primary education, revealing a significant divergence in skills characterized by an M-shaped distribution among students in the fifth and sixth grades. To address this disparity, the study employs a performance evaluation method that combines importance–performance analysis (IPA) and the refined Kano model, aiming to identify and improve upon the current deficiencies in English language teaching. Further analytical depth is provided by applying a three-factor theory, which re-evaluates the traditional five-factor framework of the Kano model, informed by a survey of 443 respondents. The analysis identified that, out of twenty evaluative items, four are critical and demand targeted enhancement efforts. The paper has identified four items for enhancement to improve English language instruction quality. Specifically, it suggests that two items from the importance–performance analysis are critically under resourced and require immediate intervention. Additionally, the paper recognizes two items from the Kano model that also need focused improvement. The study recommends swift and targeted actions to address these areas, with the goal of raising the overall standard of teaching. Based on these findings, the study proposes a set of practical recommendations aimed at increasing the overall effectiveness of teaching and fostering an environment that enhances student motivation. The ultimate goal of this study is to support English teachers in primary schools by providing strategic insights that can lead to the enhancement of teaching practices and the amelioration of English language education deficiencies.
Preparing Pre-Service Teachers for Family Engagement: Insights from the Initial Teacher Education Syllabus
Authors: Cabral Sónia , Mata Lourdes , Peixoto Francisco
Published: June 2024

Family involvement and participation in education (FIPE) profoundly impacts the quality of students’ academic and social development. Initial teacher education contribution in fostering attitudes, skills, and strategies for effective FIPE is therefore unquestionable. We aimed to find out to what extent Portuguese pre-service teachers are prepared to engage families. A document analysis was conducted to establish explicit information regarding FIPE within initial teacher education syllabi. Out of 621 syllabi across 36 master’s courses from 25 institutions, only 98 included some information on FIPE. A mere 12 syllabi, from seven institutions, exclusively addressed family–school relationships. Our study covered over 87% of the master’s courses and syllabi, exposing inconsistencies in their educational aims, content, and recommended literature. These findings highlight discrepancies within the initial teacher education syllabi and underscore the need for the enhanced training of pre-service teachers in FIPE. It is crucial to promote more in-depth and explicit syllabi to promote effective family engagement and enrich initial teacher education programs.
Adaption BERT for Medical Information Processing with ChatGPT and Contrastive Learning
Authors: Min Lingtong , Fan Ziman , Dou Feiyang , Sun Jiaao , Luo Changsheng , Lv Qinyi
Published: June 2024

Calculating semantic similarity is paramount in medical information processing, and it aims to assess the similarity of medical professional terminologies within medical databases. Natural language models based on Bidirectional Encoder Representations from Transformers(BERT) offer a novel approach to semantic representation for semantic similarity calculations. However, due to the specificity of medical terminologies, these models often struggle with accurately representing semantically similar medical terms, leading to inaccuracies in term representation and consequently affecting the accuracy of similarity calculations. To address this challenge, this study employs Chat Generative Pre-trained Transformer (ChatGPT) and contrastive loss during the training phase to adapt BERT, enhancing its semantic representation capabilities and improving the accuracy of similarity calculations. Specifically, we leverage ChatGPT-3.5 to generate semantically similar texts for medical professional terminologies, incorporating them as pseudo-labels into the model training process. Subsequently, contrastive loss is utilized to minimize the distance between relevant samples and maximize the distance between irrelevant samples, thereby enhancing the performance of medical similarity models, especially with limited training samples. Experimental validation is conducted on the open Electronic Health Record (OpenEHR) dataset, randomly divided into four groups to verify the effectiveness of the proposed methodology.
Application of Gray Wolf Particle Filter Algorithm Based on Golden Section in Wireless Sensor Network Mobile Target Tracking
Authors: Peng Duo , Xie Kun , Liu Mingshuo
Published: June 2024

In order to address the issue of low tracking accuracy caused by particle depletion in the particle filter, a mobile target tracking algorithm tailored for wireless sensor networks (WSNs) is presented. This algorithm, based on the golden-section gray wolf particle filter (PF), represents a novel approach to target tracking. The algorithm’s originality lies in its ability to guide the particle swarm toward regions of higher weights, thereby striking a balance between global and local exploration capabilities. This not only alleviates issues related to sample depletion and local extrema but also enhances the diversity of the particle swarm, significantly improving tracking performance. To assess the effectiveness of the proposed algorithm, a series of simulation experiments were conducted, comparing it with the extended Kalman filter (EKF) and the standard PF algorithm. The experiments employed a constant velocity circular motion model (CM) for filtering and tracking. The root mean square error metric demonstrated a significant reduction in error of 57% and 37% in comparison to the extended Kalman filter (EKF) and the particle filter (PF), respectively. This serves to illustrate the superiority of our method in enhancing tracking accuracy.
Reinforcement Learning-Based Resource Allocation for Multiple Vehicles with Communication-Assisted Sensing Mechanism
Authors: Fan Yuxin , Fei Zesong , Huang Jingxuan , Wang Xinyi
Published: June 2024

Autonomous vehicles (AVs) can be equipped with Integrated sensing and communications (ISAC) devices to realize sensing and communication functions simultaneously. Time-division ISAC (TD-ISAC) is advantageous due to its ease of implementation, efficient deployment and integration into any system. TD-ISAC greatly enhances spectrum efficiency and equipment utilization and reduces system energy consumption. In this paper, we propose a communication-assisted sensing mechanism based on TD-ISAC to support multi-vehicle collaborative sensing. However, there are some challenges in applying TD-ISAC to AVs. First, AVs should allocate resources for sensing and communication in a dynamically changing environment. Second, the limited spectrum resources bring the problem of mutual interference of multi-vehicle signals. To address these issues, we construct a multi-vehicle signal interference model, formulate an optimization problem based on the partially observable Markov decision process (POMDP) framework and design a decentralized dynamic allocation scheme for multi-vehicle time–frequency resources based on a deep reinforcement learning (DRL) algorithm. Simulation results show that the proposed scheme performs better in miss detection probability and average system interference power compared to the DRQN algorithm without the communication-assisted sensing mechanism and the random algorithm without reinforcement learning. We can conclude that the proposed scheme can effectively allocate the resources of the TD-ISAC system and reduce interference between multiple vehicles.
A Review of Energy-Efficient Technologies and Decarbonating Solutions for Process Heat in the Food Industry
Authors: Faraldo François , Byrne Paul
Published: June 2024

Heat is involved in many processes in the food industry: drying, dissolving, centrifugation, extraction, cleaning, washing, and cooling. Heat generation encompasses nearly all processes. This review first presents two representative case studies in order to identify which processes rely on the major energy consumption and greenhouse gas (GHG) emissions. Energy-saving and decarbonating potential solutions are explored through a thorough review of technologies employed in refrigeration, heat generation, waste heat recovery, and thermal energy storage. Information from industrial plants is collected to show their performance under real conditions. The replacement of high-GWP (global warming potential) refrigerants by natural fluids in the refrigeration sector acts to lower GHG emissions. Being the greatest consumers, the heat generation technologies are compared using the levelized cost of heat (LCOH). This analysis shows that absorption heat transformers and high-temperature heat pumps are the most interesting technologies from the economic and decarbonation points of view, while waste heat recovery technologies present the shortest payback periods. In all sectors, energy efficiency improvements on components, storage technologies, polygeneration systems, the concept of smart industry, and the penetration of renewable energy sources appear as valuable pathways.
Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review
Authors: Recalde Angel , Cajo Ricardo , Velasquez Washington , Alvarez-Alvarado Manuel S.
Published: June 2024

This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.
Enhancing Energy Efficiency and Resource Recovery in Wastewater Treatment Plants
Authors: Twi-Yeboah Nigel , Osei Dacosta , Dontoh William H. , Asamoah George Adu , Baffoe Janet , Danquah Michael K.
Published: June 2024

This paper explores the significant role of Wastewater Treatment Plants (WWTPs) in achieving environmental sustainability, with a particular focus on enhancing energy efficiency, resource recovery, and water reuse. WWTPs are crucial for removing pollutants and recovering resources from wastewater, thereby protecting public health and biodiversity. However, they are also associated with high operational costs, substantial carbon footprints, and energy-intensive processes. This article delves into various strategies and technologies to overcome these challenges, aiming to transform WWTPs from energy consumers to energy-efficient resource recovery hubs. Techniques such as anaerobic digestion and the use of advanced oxidation processes and microbial fuel cells are investigated for their potential in energy recovery and efficiency enhancement. Success stories from around the globe are highlighted to demonstrate the feasibility of transitioning to energy-positive WWTP operations. The integration of water reuse systems is also discussed, highlighting recent advancements that enable treated wastewater to be repurposed for agricultural, industrial, and potable uses, thereby promoting sustainability and water conservation. This paper emphasizes the importance of integrating cutting-edge energy management practices to minimize environmental impacts, reduce operational costs, and contribute to a more sustainable water sector.
Influence of Ambient Temperature on the Reliability of Overhead LV Power Lines with Bare Conductors
Authors: Banasik Kornelia
Published: June 2024

The article presents a study on the influence of weather factors (ambient temperature) on the operational reliability of overhead low-voltage power lines with bare conductors. A method for determining the average failure intensity, average failure duration, average renewal intensity, and failure rate of overhead low-voltage power lines with bare conductors as a function of ambient temperature is presented. Based on many years of observations of power lines operated in electric power distribution networks in Poland, the empirical values of the above-mentioned reliability indicators were determined. An analysis of empirical distribution compliance with the assumed theoretical model was also carried out. The reliability studies conducted showed that the highest failure intensity of the considered power lines occurred at temperatures commonly found in Poland.
The Impact of Temperature on the Performance and Reliability of Li/SOCl2 Batteries
Authors: Sun Yongquan , Qin Xinkun , Li Lin , Zhang Youmei , Zhang Jiahai , Qi Jia
Published: June 2024

The performance and reliability of lithium thionyl chloride (Li/SOCl2) batteries are significantly affected by temperature, but the reliability level and failure mechanisms of Li/SOCl2 batteries remain unclear. In this study, Weibull distribution statistics were used to infer the life expectancy of Li/SOCl2 batteries at different temperatures. Additionally, the battery failure mechanism was analyzed using electrochemical impedance spectroscopy (EIS). It is found that under the discharge condition of 7.5 kΩ load, the mean time between failures (MTBF) and reliable life of the battery decreased with increasing operating temperature. Under the discharge condition of 750 Ω load, the MTBF of the battery peaked at 60 °C. Furthermore, the influence of temperature on the voltage output characteristics of Li/SOCl2 batteries and the voltage hysteresis were analyzed. Both the battery output voltage and the hysteresis effect increased with rising temperature. This is because high temperature accelerates internal battery reactions, thus altering the formation process of the passivation film on the lithium metal negative electrode.
The Impact of Biochar Additives and Fat-Emulsifying Substances on the Efficiency of the Slaughterhouse Waste Biogasing Process
Authors: Kuboń Maciej , Komorowska Monika , Niemiec Marcin , Sikora Jakub , Szeląg-Sikora Anna , Olech Elżbieta , Molik Edyta , Gajda Jakub
Published: June 2024

Waste management in the agri-food industry is an important technological and environmental problem. Slaughterhouse waste is particularly problematic, as it contains significant amounts of proteins and lipids, neither of which constitute a good substrate for methane production. The physical properties of mixtures of fat and polar solvents inhibit the methanogenesis process. The aim of this research was to assess the impact of the addition of fat emulsifiers and biochar on the amount of biogas produced and the level of carbon conversion per unit of waste weight. The assumed goal was achieved based on a laboratory experiment using static methane fermentation, carried out in accordance with the methodology of the DIN 38414/S8 standard. The results of the experiment indicate that the addition of fat emulsifiers increased the biogas yield from slaughterhouse waste from approximately 370 to 430 dm3 per 2 kg dry weight of waste. Each technological variant resulted in an increase in the amount of carbon that was transformed in the methanogenesis process. Although the level of carbon transformation in the methanogenesis process increased by approximately 20% in objects with emulsifier addition, the use of biochar and fat emulsifiers did not change the amount of methane production. Within the assumed system limits, therefore, the use of fat emulsifiers and biochar seems to be pointless. However, the use of the developed technology can improve the efficiency of biogas production by up to 18% and shorten the process by 5 days. Assuming the continuous operation of the biogas plant, the use of the developed technology will increase the efficiency of biogas production from slaughterhouse waste in the long term by over 30% without the need to modify the infrastructure in the biogas plant.
Methods and Validation Techniques of Chemical Kinetics Models in Waste Thermal Conversion Processes
Authors: Skrzyniarz Magdalena , Sajdak Marcin , Biniek-Poskart Anna , Skibiński Andrzej , Krakowiak Marlena , Piotrowski Andrzej , Krasoń Patrycja , Zajemska Monika
Published: June 2024

This article discusses the potential of using computer-simulation methods in processes such as thermal waste conversion, i.e., pyrolysis, gasification, combustion and torrefaction. These methods are gaining in importance, among others, due to the difficulties in execution and high costs associated with conducting experimental research in real conditions or the need to obtain detailed data on the phenomenon under study in a relatively short time. Computer simulation also allows for numerous errors to be avoided, such as those that may occur during optimization activities, the effects of which may have serious consequences, both economic and environmental. In addition to their many advantages, the limitations and disadvantages of using computer-simulation methods were also indicated, mainly related to the interpretation and validation of the results obtained using modelling. Owing to the complexity of the phenomena occurring during thermal conversion, special attention was focused on models based on chemical kinetics, thanks to which it is possible to predict the quantitative and qualitative composition of products in these processes. The aim of this research is to identify the research gap in the field of issues related to models of chemical kinetics of thermal waste conversion processes.
Theoretical Study of the D-T Fuel Burning Rate in Z-Pinch Facilities with Magneto-Inertial Confinement
Authors: Bayakhmetov Olzhas , Azamatov Assylkhan
Published: June 2024

This paper focuses on the theoretical study of the burning rate of D-T fuel in Z-pinch devices with magneto-inertial confinement. The investigated nuclear fusion process involved fast laser ignition of a mixed D-T fuel contained in a capsule at a temperature of 10 keV, influenced by a strong electromagnetic field. The D-T, D-D, D-3He, 3He-3He, and T-T fusion reactions were employed in the calculations. Based on modern experimental fit data of nuclear fusion reaction rates, the particle and energy balance equations, along with their numerical solutions, were considered, utilizing the ion densities of charged particles such as protons, deuterium, tritium, 3He, and 4He ions. The plasma was in a hot, ultra-dense state, under the quasi-neutrality condition, with initial deuterium and tritium densities of 5×1023 cm−3 and an electron density of 10×1023 cm−3. The ion and electron temperatures were considered equal in this paper. The time dependencies of the ion densities, plasma temperature, energy yield from charged ions and neutrons, fusion power density, and bremsstrahlung radiation loss were investigated.
Techno-Economic Analysis of Hydrogen as a Storage Solution in an Integrated Energy System for an Industrial Area in China
Authors: Zeng Jincan , Liu Xiaoyu , Liu Minwei , Liu Xi , Huang Guori , Yao Shangheng , He Gengsheng , Shang Nan , Guo Fuqiang , Wang Peng
Published: June 2024

This study proposes four kinds of hybrid source–grid–storage systems consisting of photovoltaic and wind energy, and a power grid including different batteries and hydrogen storage systems for Sanjiao town. HOMER-PRO was applied for the optimal design and techno-economic analysis of each case, aiming to explore reproducible energy supply solutions for China’s industrial clusters. The results show that the proposed system is a fully feasible and reliable solution for industry-based towns, like Sanjiao, in their pursuit of carbon neutrality. In addition, the source-side price sensitivity analysis found that the hydrogen storage solution was cost-competitive only when the capital costs on the storage and source sides were reduced by about 70%. However, the hydrogen storage system had the lowest carbon emissions, about 14% lower than the battery ones. It was also found that power generation cost reduction had a more prominent effect on the whole system’s NPC and LCOE reduction. This suggests that policy support needs to continue to push for generation-side innovation and scaling up, while research on different energy storage types should be encouraged to serve the needs of different source–grid–load–storage systems.
Improving Photovoltaic Power Prediction: Insights through Computational Modeling and Feature Selection
Authors: Amiri Ahmed Faris , Chouder Aissa , Oudira Houcine , Silvestre Santiago , Kichou Sofiane
Published: June 2024

This work identifies the most effective machine learning techniques and supervised learning models to estimate power output from photovoltaic (PV) plants precisely. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest regressor, Support Vector regression (SVR), Multi-layer Perceptron regressor (MLP), Linear regressor (LR), Gradient Boosting, k-Nearest Neighbors regressor (KNN), Ridge regressor (Rr), Lasso regressor (Lsr), Polynomial regressor (Plr) and XGBoost regressor (XGB). The methodology applied starts with meticulous data preprocessing steps to ensure dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root-Mean-Squared Error (RMSE), Normalized Root-Mean-Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R2), Integral Absolute Error (IAE), and Standard Deviation of the Difference (SDD). Among the models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048%, and an R2 score of 0.968. Furthermore, the Random Forest regressor (the best-performing model) is integrated into a MATLAB application for real-time predictions, enhancing its usability and accessibility for a wide range of applications in renewable energy.
Data-Driven Occupancy Profile Identification and Application to the Ventilation Schedule in a School Building
Authors: Vassiljeva Kristina , Matson Margarita , Ferrantelli Andrea , Petlenkov Eduard , Thalfeldt Martin , Belikov Juri
Published: June 2024

Facing the current sustainability challenges requires reduction in building stock energy usage towards achieving the European Green Deal targets. This can be accomplished by adopting techniques such as fault detection and diagnosis and efficiency optimization. Taking an Estonian school as a case study, an occupancy-based algorithm for scheduling ventilation operations in buildings is here developed starting only from energy use data. The aim is optimizing the system’s operation according to occupancy profiles while maintaining a comfortable indoor climate. By relying only on electricity meters without using carbon dioxide or occupancy sensors, we use the historical data of a school to develop a DBSCAN-based clustering algorithm that generates consumption profiles. A novel occupancy estimation algorithm, based on threshold and time-series methods, then creates 12 occupancy schedules that are either based on classical detection with an on-off method or on occupancy estimation for demand-controlled ventilation. We find that the latter replaces the 60% capacity of current on-off schedules by 30% or even 0%, with energy savings ranging from 3.5% to 66.4%. The corresponding costs are reduced from 18.1% up to 62.6%, while still complying with current national regulations for indoor air quality. Remarkably, our method can immediately be extended to other countries, as it relies only on occupancy schedules that ignore weather and other location-specific factors.
Bibliometric Analysis: The Main Steps
Authors: Passas Ioannis
Published: June 2024

Bibliometric analysis is a systematic study carried out on scientific literature for the identification of patterns, trends, and impact within a certain field. Major steps include data collection from relevant databases, data cleaning and refining, and subjecting data to various bibliometric methods—an ensuing step in the generation of meaningful information. Bibliometric analysis is an increasingly popular and thorough technique for examining and assessing massive amounts of scientific data, which is being used more and more in research. This entry thoroughly introduces bibliometric methodology, emphasizing its numerous methodologies. It also provides a set of reliable, step-by-step instructions for confidently performing bibliometric analysis. Furthermore, we investigate the suitable use of bibliometric analysis as an alternative to systematic literature reviews. This entry aims to be a useful tool for learning about the methods and approaches that may be used to perform research studies that use bibliometric analysis, particularly in the fields of academic study.
An Investigation of Increased Power Transmission Capabilities of Elastic–Plastic-Designed Press–Fit Connections Using a Detachable Joining Device
Authors: Falter Jan , Herburger Daniel , Binz Hansgeorg , Kreimeyer Matthias
Published: June 2024

Drive systems are an important part of general mechanical engineering, automotive engineering, and various other fields, with shaft–hub connections being an important part of such systems. Decisive aspects in the development of such systems today are, for example, high transmittable forces and torques, low masses, and the cheapest possible production of components. A possibly threefold increase in the force and torque transmission capacity can be achieved by using press–fit connections with an elastic–plastic design as opposed to regular elastically designed alternatives. An elastic–plastic design of the press–fit connection is achieved by using a large interference. A large transition geometry on the shaft (which replaces the conventional chamfer) is required to join such an interference. The material and space requirements have a negative impact on lightweight applications and limited building spaces. Therefore, the objective of the research presented in this paper is to design and analyze a detachable joining device that substitutes this geometry. A simulation study was conducted to determine the geometry of the joining device that improves the stress state and consequently the force and torque transmission capacity of the connection. Moreover, the influence of manufacturing tolerances of the joining device and the shaft, corresponding risks, and measures to mitigate them are analyzed using finite element analysis. The results show that large transition radii, enabled by using a joining device, lead to a homogenous distribution of plastic strain and pressure in the press–fit connection, even for large interferences ξ and soft hub materials like wrought aluminum alloys. The influence of manufacturing tolerances on the stress state was quantified, leading to design guidelines that minimize the risk of, e.g., the front face collision of a shaft and hub, while maximizing the power transmission of the connection. The results show the capability of a detachable joining device to enable elastic–plastic press–fit connections and the corresponding threefold increase in the force and torque transmission capacity in lightweight applications, resulting from the substitution of the installation space consuming and mass increasing the transition geometry of the shaft.
Soil Chemical Quality in Integrated Production Systems with the Presence of Native and Exotic Tree Components in the Brazilian Eastern Amazon
Authors: Souza Ivanderlete Marques de , Sagrilo Edvaldo , de Oliveira Júnior José Oscar Lustosa , Araújo Maria Diana Melo , Muniz Luciano Cavalcante , Costa Joaquim Bezerra , Pompeu Roberto Cláudio Fernandes Franco , de Sousa Daiane Conceição , de Andrade Hosana Aguiar Freitas , de Oliveira Neto Edson Dias , Leite Luiz Fernando Carvalho , Blanco Flávio Favaro , Lima Paulo Sarmanho da Costa , Souza Henrique Antunes de
Published: June 2024

Conservation systems involving trees enhance the sustainability of tropical soils. However, little is known on the effect of integrated systems with native and exotic trees on soil chemical quality in the eastern Amazon. We aimed to measure changes in soil chemical quality in integrated production systems in Pindaré-Mirim, Maranhão, Brazil. This study was carried out in 2017 and 2018, evaluating (i) perennial pasture; (ii) crop–livestock–forest integration-I (CLFI-I)—eucalyptus rows interspersed with maize + Urochloa brizantha intercropping; (iii) CLFI-II—babassu palm trees (Attalea speciosa Mart.) with maize + Megathyrsus maximus intercropping; and (iv) maize + M. maximus intercropping. Soil chemical attributes at depths of 0.00–0.10 m, 0.10–0.20 m, 0.20–0.30 m, and 0.30–0.50 m, forage productivity, and soil cover were evaluated. CLFI-II promoted the highest soil organic matter concentration in topsoil and highest pH, lowest Al3+ levels, and potential acidity (H+Al) at all soil depths. Soil under pasture showed the highest N, K+, Ca2+ concentrations, sum of bases, and cation exchange capacity. Changes in CLFI-II are associated with the babassu palm’s ability to modulate the surrounding environment, giving the species a competitive advantage in anthropic environments. The time of adoption is crucial for improving soil fertility in the Brazilian eastern Amazon. Sustainable production systems in the region must comply with long-term management plans.
The Regulation of Superconducting Magnetic Energy Storages with a Neural-Tuned Fractional Order PID Controller Based on Brain Emotional Learning
Authors: Safari Ashkan , Sorouri Hoda , Oshnoei Arman
Published: June 2024

Intelligent control methodologies and artificial intelligence (AI) are essential components for the efficient management of energy storage modern systems, specifically those utilizing superconducting magnetic energy storage (SMES). Through the implementation of AI algorithms, SMES units are able to optimize their operations in real time, thereby maximizing energy efficiency. To have a more advanced understanding of this issue, DynamoMan is presented in this paper. For use with SMES systems, DynamoMan, an Artificial Neural Network (ANN)-tuned Fractional Order PID Brain Emotional Learning-Based Intelligent Controller (ANN-FOPID-BELBIC), has been developed. ANN tuning is employed to optimize the key settings of the reward/penalty generator of a BELBIC, which are important for its overall efficacy. Following this, DynamoMan is integrated into the SMES control system and compared to scenarios in which a BELBIC, PID, PI, and P are utilized. The findings indicate that DynamoMan performs considerably better than other models, demonstrating robust and control attributes alongside a considerably reduced period of settling time, especially when incorporated with the power grid.
Repurposing Synthetic Acetaminophen Derivatives Containing a Benzothiazole Scaffold as an Alternative Therapy for Infectious Diarrhea Caused by Drug-Resistant Shigella Species
Authors: Pone Kamdem Boniface , Pinlap Brice Rostan , Noumboue Kouamou Bijou-Lafortune , Youbi Kamche Aubin , Kuate Boris Arnaud , Tsemeugne Joseph , Ngomo Orleans , Mkounga Pierre , Fekam Boyom Fabrice
Published: June 2024

Diarrhea remains one of the leading causes of mortality worldwide, especially among children. Accumulated evidence has shown that Shigella species are the most prevalent bacteria responsible for diarrhea in developing countries. Antimicrobial therapy is necessary for Shigella infections; however, the development of resistance against current drugs justifies the pressing need to search for alternative medications. In this study, we have applied antibacterial phenotypic screening to identify potent anti-Shigella compounds across a broad chemical diversity, including selected acetaminophen derivatives containing a benzothiazole backbone, and their combination with certain antibiotics. As a result, two acetaminophen derivatives containing a benzothiazole backbone (4a and 4b) inhibited the growth of Shigella flexneri with a common MIC value of 12.5 µg/mL. These compounds were established through a time-kill kinetics study to be potentially bactericidal. Meanwhile, the 2-aminobenzothiazoles (1a and 1b) used for the synthesis of compounds 4 (a and b) were found to be poorly active (MIC: 100 µg/mL) against this pathogen. Combination studies of 4a and 4b with the least effective antibiotics (ceftriaxone and cotrimoxazole) demonstrated synergistic anti-Shigella activity with MIC values decreasing from 12.5 to 0.781 μg/ mL. The present study demonstrates that the azobenzothiazole dyes 4 (a and b) can be repurposed as potential anti-Shigella compounds, thus providing potential chemical pharmacophores for the discovery of drugs against infectious diarrhea caused by Shigella and other enteric pathogens, especially in developing countries.
High-Temperature-Resistant Profile Control System Formed by Hydrolyzed Polyacrylamide and Water-Soluble Phenol-Formaldehyde Resin
Authors: Li Xuanran , Liu Shanglin , Zhang Juan , Han Shujun , Zhao Lun , Xu Anzhu , Wang Jincai , Zhou Fujian , Li Minghui
Published: June 2024

To realize the effective profile control of a heavy oil reservoir, hydrolyzed polyacrylamide (HPAM) and water-soluble phenol-formaldehyde resin (PR) were chosen to prepare the profile control system, which gelled at medium or low temperatures and existed stably at high temperatures in the meantime. The effects of phenolic ratios, PR concentration, and HPAM concentration on the formation and strength of the gels were systematically studied by the gel-strength code method and rheological measurements. And the microstructure of the gels was investigated by scanning electron microscope measurements. The results showed that the gelling time of the HPAM-PR system was 13 h at 70 °C. The formed gel could stay stable for 90 days at 140 °C. In addition, the gels showed viscoelastic properties, and the viscosity reached 18,000 mPa·s under a 1.5 s−1 shearing rate due to their three-dimensional cellular network structure. The formation of the gels was attributable to the hydroxyl groups of the PR crosslinking agent, which could undergo the dehydration condensation reaction with amide groups under non-acidic conditions and form intermolecular crosslinking with HPAM molecules. And the organic crosslinker gel system could maintain stability at higher temperatures because covalent bonds formed between molecules.
Correlation Analysis of Genetic Mutations and Galectin Levels in Breast Cancer Patients
Authors: Markalunas Ella , Arnold David , Funkhouser Avery , Martin Julie , Shtutman Michael , Edenfield W. , Blenda Anna
Published: June 2024

Galectins are innate immune system regulators associated with disease progression in cancer. This paper aims to investigate the correlation between mutated cancer-critical genes and galectin levels in breast cancer patients to determine whether galectins and genetic profiles can be used as biomarkers for disease and potential therapy targets. Prisma Health Cancer Institute’s Biorepository provided seventy-one breast cancer samples, including all four stages spanning the major molecular subtypes and histologies. Hotspot mutation statuses of cancer-critical genes were determined using multiplex PCR in tumor samples from the same patients by Precision Genetics and the University of South Carolina Functional Genomics Core Facility. The galectin-1, -3, and -9 levels in patients’ sera were analyzed using Enzyme-linked Immunosorbent Assay (ELISA). An analysis was performed using JMP software to compare mean and median serum galectin levels between samples with and without specific cancer-critical genes, including pooled t-test, Wilcoxon Rank Sum Test, ANOVA, and Steel Dwass Test (α=0.05). Our analysis indicates that KIT mutations correlate with elevated serum levels of galectin-9 in patients with breast cancer. In patients with Luminal A subtype, FLT3 mutation correlates with lower serum galectin-1 and -9 levels and TP53 mutations correlate with higher serum galectin-3 levels. Patients with invasive ductal carcinoma had significantly higher serum galectin-3 levels than patients with ductal carcinoma in situ. Patients with both TP53 and PIK3CA mutations exhibit elevated serum galectin-3 levels, while patients with one or neither mutation show no significant difference in serum galectin-3 levels. In addition, metastatic breast cancer samples were more likely to have a KIT or PIK3CA mutation compared to primary breast cancer samples. The relationship between genetic mutations and galectin levels has the potential to identify appropriate candidates for combined therapy, targeting genetic mutations and galectins. Further understanding of the effect of genetic mutations and galectin levels on cancer progression and metastasis could aid in the search for biomarkers for breast cancer diagnosis, disease progression, and prognosis.