bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images
Authors: Alsalatie Mohammed , Alquran Hiam , Mustafa Wan Azani , Zyout Ala’a , Alqudah Ali Mohammad , Kaifi Reham , Qudsieh Suhair
Published: August 2023

One of the most widespread health issues affecting women is cervical cancer. Early detection of cervical cancer through improved screening strategies will reduce cervical cancer-related morbidity and mortality rates worldwide. Using a Pap smear image is a novel method for detecting cervical cancer. Previous studies have focused on whole Pap smear images or extracted nuclei to detect cervical cancer. In this paper, we compared three scenarios of the entire cell, cytoplasm region, or nucleus region only into seven classes of cervical cancer. After applying image augmentation to solve imbalanced data problems, automated features are extracted using three pre-trained convolutional neural networks: AlexNet, DarkNet 19, and NasNet. There are twenty-one features as a result of these scenario combinations. The most important features are split into ten features by the principal component analysis, which reduces the dimensionality. This study employs feature weighting to create an efficient computer-aided cervical cancer diagnosis system. The optimization procedure uses the new evolutionary algorithms known as Ant lion optimization (ALO) and particle swarm optimization (PSO). Finally, two types of machine learning algorithms, support vector machine classifier, and random forest classifier, have been used in this paper to perform classification jobs. With a 99.5% accuracy rate for seven classes using the PSO algorithm, the SVM classifier outperformed the RF, which had a 98.9% accuracy rate in the same region. Our outcome is superior to other studies that used seven classes because of this focus on the tissues rather than just the nucleus. This method will aid physicians in diagnosing precancerous and early-stage cervical cancer by depending on the tissues, rather than on the nucleus. The result can be enhanced using a significant amount of data.
Single Quasi–Symmetrical LED with High Intensity and Wide Beam Width Using Diamond–Shaped Mirror Refraction Method for Surgical Fluorescence Microscope Applications
Authors: Ju Minki , Yoon Kicheol , Lee Sangyun , Kim Kwang Gi
Published: August 2023

To remove tumors with the same blood vessel color, observation is performed using a surgical microscope through fluorescent staining. Therefore, surgical microscopes use light emitting diode (LED) emission and excitation wavelengths to induce fluorescence emission wavelengths. LEDs used in hand–held type microscopes have a beam irradiation range of 10° and a weak power of less than 0.5 mW. Therefore, fluorescence emission is difficult. This study proposes to increase the beam width and power of LED by utilizing the quasi–symmetrical beam irradiation method. Commercial LED irradiates a beam 1/r2 distance away from the target (working distance). To obtain the fluorescence emission probability, set up four mirrors. The distance between the mirrors and the LED is 5.9 cm, and the distance between the mirrors and the target is 2.95 cm. The commercial LED reached power on target of 8.0 pW within the wavelength band of 405 nm. The power reaching the target is 0.60 mW in the wavelength band of 405 nm for the LED with the beam mirror attachment method using the quasi–symmetrical beam irradiation method. This result is expected to be sufficient for fluorescence emission. The light power of the mirror was increased by approximately four times.
Serum Adropin Levels and Body Mass Composition in Kidney Transplant Recipients—Are There Sex Differences?
Authors: Radić Josipa , Lovrić Kojundžić Sanja , Gelemanović Andrea , Vučković Marijana , Budimir Mršić Danijela , Šupe Domić Daniela , Novaković Maja , Radić Mislav
Published: August 2023

Adropin is a secretory peptide that regulates glucose, lipid, and protein metabolism, which is closely related to obesity, insulin resistance, dyslipidemia, and atherogenesis. The serum adropin level is related to sex and depends upon nutritional preferences. This study aims to determine the association between serum adropin levels and body composition parameters in kidney transplant recipients (KTRs), especially emphasizing sex differences. Our case–control study involved 59 KTRs (28 postmenopausal women and 31 men) who were divided into two groups according to sex, and each group of those KTRs was further divided into higher or lower adropin values than the mean value in each sex group. Univariate regression showed a negative association of adropin levels with most anthropometric and body composition parameters in men’s KTRs. Contrary to this, the serum adropin level was negatively associated only with phase angle in postmenopausal female KTRs. Multivariate regression showed that skeletal muscle mass and phase angle were the only negative predictors in women’s KTRs, whereas in men, negative predictors were BMI and body water. These findings imply that adropin could have a different impact on metabolic homeostasis in KTRs regarding sex and could be considered a negative predictor of body composition in KTRs.
Comparison of Ki67 Proliferation Index in Gastrointestinal Non-Hodgkin Large B-Cell Lymphomas: The Conventional Method of Evaluation or AI Evaluation?
Authors: Cristian Miruna , Așchie Mariana , Deacu Mariana , Boșoteanu Mădălina , Bălțătescu Gabriela Izabela , Stoica Andreea Georgiana , Nicolau Anca Antonela , Poinăreanu Ionuț , Orășanu Cristian Ionuț
Published: August 2023

The most common NHL subtype in SEEU is DLBCL (39%), and it manifests with a variety of cellular morphologies and a high proliferation index. Also, the GI tract is the most common site of extranodal NHLs, and most NHLs involving the GI tract are of B-cell lineage, of which diffuse large B-cell lymphoma is the most common subtype, irrespective of location. The last few years have seen digital pathology as a vital technology that has a positive impact on diagnostics, but studies on the use of DP for lymphoma identification, however, are still restricted to only determining whether a tumor is present or absent. Using the example of cases of malignant NHL, we aim to investigate the diagnostic utility of DP using QuPath software in evaluating the proliferation index and the prognostic significance and to show that improved visualization and analysis contribute to the convergence of these complementary diagnostic modalities for lymphomas. The average proliferation index (Ki67) was 58.33% with values between 10% and 85%. After the stratification of the cases, an increased proliferation index was observed in the majority of cases (53.33%), and this aspect was associated with the advanced age of the patients (p = 0.045). Visual assessment provides lower Ki67 values than automated digital image analysis. However, the agreement coefficient between the conventional method and the AI method indicates an excellent level of reliability (ICC1–0.970, ICC2–0.990). The multivariate analysis revealed that in the cases where the proliferation index Ki67 is high (˃70%), the IPI score represents an important risk factor predicting mortality (HR = 10.597, p = 0.033).
Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer
Authors: Kim Moonsik , Jeong Ji Yun , Seo An Na
Published: August 2023

Despite advances in diagnostic imaging, surgical techniques, and systemic therapy, gastric cancer (GC) is the third leading cause of cancer-related death worldwide. Unfortunately, molecular heterogeneity and, consequently, acquired resistance in GC are the major causes of failure in the development of biomarker-guided targeted therapies. However, by showing promising survival benefits in some studies, the recent emergence of immunotherapy in GC has had a significant impact on treatment-selectable procedures. Immune checkpoint inhibitors (ICIs), widely indicated in the treatment of several malignancies, target inhibitory receptors on T lymphocytes, including the programmed cell death protein (PD-1)/programmed death-ligand 1 (PD-L1) axis and cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and release effector T-cells from negative feedback signals. In this article, we review currently available predictive biomarkers (including PD-L1, microsatellite instability, Epstein–Barr virus, and tumor mutational burden) that affect the ICI treatment response, focusing on PD-L1 expression. We further briefly describe other potential biomarkers or mechanisms for predicting the response to ICIs in GC. This review may facilitate the expansion of the understanding of biomarkers for predicting the response to ICIs and help select the appropriate therapeutic approaches for patients with GC.
Dynamic Analysis and Experiment of Multiple Variable Sweep Wings on a Tandem-Wing MAV
Authors: Gao Liang , Zhu Yanhe , Zang Xizhe , Zhang Junming , Chen Boyang , Li Liyi , Zhao Jie
Published: August 2023

The current morphing technologies are mostly regarded as auxiliary tools, providing additional control torques to enhance the flight maneuverability of unmanned aerial vehicles (UAVs), and they cannot exist independently of the traditional control surfaces. In this paper, we propose a tandem-wing micro aerial vehicle (MAV) with multiple variable-sweep wings, which can reduce the additional inertia forces and moments and weaken the dynamic coupling between longitudinal and lateral motion while the MAV morphs symmetrically for pitch control or asymmetrically for roll control, thereby flying without the traditional aileron and elevator. First, load experiments were conducted on the MAV to verify the structural strength of the multiple variable sweep wings, and the control moments caused by the morphing of the MAV were presented through numerical simulations. Then, the effects caused by symmetric and asymmetric morphing were investigated via dynamic response simulations based on the Kane dynamic model of the MAV, and the generated additional inertia forces and moments were also analyzed during morphing. Finally, dynamic response experiments and open-loop flight experiments were conducted. The experimental results demonstrated that the morphing mode in this study could weaken the coupling between the longitudinal and lateral dynamics and that it was feasible for attitude control without the traditional aileron and elevator while flying.
Assessment of UAS Photogrammetry and Planet Imagery for Monitoring Water Levels around Railway Tracks
Authors: Arroyo-Mora Juan , Kalacska Margaret , Roghani Alireza , Lucanus Oliver
Published: August 2023

High water levels near railway tracks can be a major factor affecting the safety of train passage. Water conditions near the tracks are normally monitored through visual inspections. However, this method is limited in spatial coverage and may not provide comparable information over time. We evaluated the utility of satellite imagery (Planet Dove constellation at 3 m pixel size) at the landscape level to assess overall water surface area along railway tracks. Comparatively, we evaluated the use of Structure- from-Motion 3D point clouds and high spatial detail orthomosaics (3 cm) generated from a commercial off-the-shelf Unmanned Aerial System (UAS) (DJI M300 RTK) for measuring vertical water level changes and extent of surface water, respectively, within the right-of-way of a railway line in Ontario, Canada, in areas prone to high water level and flooding. Test sites of varied lengths (~180 m to 500 m), were assessed four times between June and October 2021. Our results indicate that the satellite imagery provides a large-scale overview regarding the extent of open water in wetlands at long distances from the railway tracks. Analysis of the UAS derived 3D point cloud indicates that changes in water level can be determined at the centimeter scale. Furthermore, the spatial error (horizontal and vertical alignments) between the multi-temporal UAS data collections between sites was less than 3 cm. Our research highlights the importance of using consistent UAS data collection protocols, and the significant potential of commercial off-the-shelf UAS systems for water level monitoring along railway tracks.
Drone-Based Environmental Emergency Response in the Brazilian Amazon
Authors: Custodio Janiele , Abeledo Hernan
Published: August 2023

This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation model with mobile servers assumes a centralized network operated out of sight by first responders and government agents. The optimization problem seeks to find the minimal cost configuration that meets operational constraints and performance objectives. To test the practical applicability of the proposed model, a real-life case study was implemented for the municipality of Ji-Paraná, in the Brazilian Amazon, using demand data from a mobile whistle-blower application and from satellite imagery projects that monitor deforestation and fire incidents in the region. Experiments are performed to understand the model’s sensitivity to various demand scenarios and capacity restrictions.
Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation
Authors: Heßler Martin , Wand Tobias , Kamps Oliver
Published: August 2023

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis, which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis, and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an online adaptive manner in pre-crisis segments. The online sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint at the importance of the U.S. housing bubble as a trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states, and could work as a comparative impact rating of specific economic events.
Periodic Intermittent Adaptive Control with Saturation for Pinning Quasi-Consensus of Heterogeneous Multi-Agent Systems with External Disturbances
Authors: Du Bin , Xu Quan , Zhang Junfu , Tang Yi , Wang Lei , Yuan Ruihao , Yuan Yu , An Jiaju
Published: August 2023

A periodic intermittent adaptive control method with saturation is proposed to pin the quasi-consensus of nonlinear heterogeneous multi-agent systems with external disturbances in this paper. A new periodic intermittent adaptive control protocol with saturation is designed to control the internal coupling between the follower agents and the feedback gain between the leader and the follower. In particular, we use the saturation adaptive law: when the quasi-consensus error converges to a certain range, the adaptive coupling edge weight and the adaptive feedback gain will not be updated. Furthermore, we propose three saturated adaptive pinning control protocols. The quasi-consensus is achieved through its own pinning as long as the agents remain connected to each other. Using the Lyapunov function method and inequality technique, the convergence range of the quasi-consensus error of a heterogeneous multi-agent system is obtained. Finally, the rationality of the proposed control protocol is verified through numerical simulation. Theoretical derivation and simulation results show that the novel proposed periodic intermittent adaptive control method with saturation can successfully be used to achieve the pinning of quasi-consensus of nonlinear heterogeneous multi-agent systems.
Inclusive Education Systems: The Struggle for Equity and the Promotion of Autonomy in Portugal
Authors: Carvalho Ana Eloisa , Cosme Ariana , Veiga Amélia
Published: August 2023

The universalization of schooling has triggered issues about the organization and management of more inclusive education systems. Several transnational organizations have produced guidelines that have contributed to the concept of inclusive education being incorporated into national educational policies. To identify the ideas underpinning this concept, we mobilized a policy cycle theoretical–methodological proposal. From a thematic analysis of (inter)national texts, four themes emerged: the recognition of diversity, the struggle for equity, the promotion of school autonomy and the emphasis on the participation of the educational community. In this article, we analyze the ideas underpinning the themes of struggle for equity and promotion of school autonomy to analyze the challenges and opportunities resulting from their articulation. Analysis of the Portuguese case revealed that the legal and normative framework shaped by international documents underlines a tension between these two themes, raising questions regarding the practices enacted by national and institutional actors. The findings suggest that equity is conditional on school autonomy; nevertheless, granted autonomy may not translate into improved equity. The extent to which our analysis of the Portuguese case reflects other national contexts where effective autonomy enhances equity remains to be seen.
Neural Machine Translation of Electrical Engineering Based on Integrated Convolutional Neural Networks
Authors: Liu Zikang , Chen Yuan , Zhang Juwei
Published: August 2023

Research has shown that neural machine translation performs poorly on low-resource and specific domain parallel corpora. In this paper, we focus on the problem of neural machine translation in the field of electrical engineering. To address the mistranslation caused by the Transformer model’s limited ability to extract feature information from certain sentences, we propose two new models that integrate a convolutional neural network as a feature extraction layer into the Transformer model. The feature information extracted by the CNN is fused separately in the source-side and target-side models, which enhances the Transformer model’s ability to extract feature information, optimizes model performance, and improves translation quality. On the dataset of the field of electrical engineering, the proposed source-side and target-side models improved BLEU scores by 1.63 and 1.12 percentage points, respectively, compared to the baseline model. In addition, the two models proposed in this paper can learn rich semantic knowledge without relying on auxiliary knowledge such as part-of-speech tagging and named entity recognition, which saves a certain amount of human resources and time costs.
Sum Rate Maximization for Intelligent Reflecting Surface-Assisted UAV-Enabled NOMA Network
Authors: Chen Songchao , Liu Fang , Liu Yuanan
Published: August 2023

In the next-generation network, intelligent reflecting surface (IRS), non-orthogonal multiple access (NOMA), and simultaneous wireless information and power transfer (SWIPT) are promising wireless communication techniques to effectively improve system sum rates. In traditional unmanned aerial vehicles (UAV) communication systems, the sum rate and coverage are greatly affected when there is an occlusion on the direct transmission link. To solve this problem, the IRS technology is introduced to improve the poor channel conditions. However, most of the previous research on IRS-assisted UAV to optimize system sum rate only considers frameworks that utilize the partially joint-combining techniques of IRS, NOMA, and SWIPT. In this paper, in order to further improve the sum rate of the system, we simultaneously integrate IRS, NOMA, and SWIPT technologies and establish a sum rate maximization optimization problem when the direct link is blocked. Then, an alternative optimization (AO) algorithm based on the maximizing system sum rate is proposed to solve the non-convex optimization problem, in which the IRS location and phase, the reflecting amplitude coefficient, UAV forwarding altitude, and power splitting factor are considered. To let the non-convex and non-linear function be transformed into a convex one, we first use an iterative approach to optimize the position of the IRS. After that, an optimization problem is constructed to maximize the system sum rate with the constraints of the IRS phase shifts, successful successive interference cancellation (SIC), maximum transmit power of base station (BS), and UAV. Numerical results show that the proposed algorithm outperforms the traditional orthogonal multiple access (OMA) and algorithms without IRS-assisted links in terms of the system sum rate.
Efficient ϵ-Approximate k-Flexible Aggregate Nearest Neighbor Search for Arbitrary ϵ in Road Networks
Authors: Kwon Hyuk-Yoon , Yoo Jaejun , Loh Woong-Kee
Published: August 2023

Recently, complicated spatial search algorithms have emerged as spatial-information-based applications, such as location-based services (LBS), and have become very diverse and frequent. The aggregate nearest neighbor (ANN) search is an extension of the existing nearest neighbor (NN) search; it finds the object p* that minimizes G{d(p*,qi),qi∈Q} from a set Q of M (≥1) query objects, where G is an aggregate function and d() is the distance between two objects. The flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search by introducing flexibility factor ϕ(0<ϕ≤1); it finds the object p* that minimizes G{d(p*,qi),qi∈Qϕ} from Qϕ, a subset of Q with |Qϕ|=ϕ|Q|. This paper proposes an efficient ϵ-approximate k-FANN (k≥1) search algorithm for an arbitrary approximation ratio ϵ (≥1) in road networks. In general, ϵ-approximate algorithms are expected to give an improved search performance at the cost of allowing an error ratio of up to the given ϵ. Since the optimal value of ϵ varies greatly depending on applications and cases, the approximate algorithm for an arbitrary ϵ is essential. We prove that the error ratios of the approximate FANN objects returned by our algorithm do not exceed the given ϵ. To the best of our knowledge, our algorithm is the first ϵ-approximate k-FANN search algorithm in road networks for an arbitrary ϵ. Through a series of experiments using various real-world road network datasets, we demonstrated that our approximate algorithm always outperformed the previous state-of-the-art exact algorithm and that the error ratios of the approximate FANN objects were significantly lower than the given ϵ value.
Positioning Control Strategy of Hydraulic Support Pushing System in Fully Mechanized Coal Face
Authors: Hou Tengyan , Kou Ziming , Wu Juan , Xu Peng , Zhang Buwen , Peng Yanwei
Published: August 2023

At present, the hydraulic support pushing system in coal mines usually uses an electrohydraulic directional valve as the control component. However, the existing control methods based on high-speed on–off valve, servo, and proportional control methods are not suitable for solving such problems because of the nonideal characteristics of the electrohydraulic directional valve, such as discrete input values, low switching frequency, and time delay. This paper proposes a positioning control scheme based on online predictive feedback for the control of hydraulic cylinders by electrohydraulic directional valves. In this scheme, the recursive least-squares estimation algorithm with genetic factors is used to identify the required prediction model in real time, and an improved radial basis function network based on generalized growth and shear is used to realize the online fitting of the target trajectory function. The online learning algorithm provides accurate prediction information for the switching control method, and finally, the hydraulic cylinder can be positioned near the target position using the optimal control method. By using the above methods, a well-designed model can be accurately identified, fundamentally solving the problem of control difficulties caused by the nonideal characteristics of the electrohydraulic directional valve. Finally, the effectiveness of the control scheme is verified through simulation analysis and physical experiment research, which proves that the control strategy can realize accurate and fast positioning control for the hydraulic support pushing system of a fully mechanized mining face.
Risk-Management Framework and Information-Security Systems for Small and Medium Enterprises (SMEs): A Meta-Analysis Approach
Authors: AL-Dosari Khalifa , Fetais Noora
Published: August 2023

Information-technology (IT) security standards are regularly updated in a rapidly changing technological world to maintain pace with advanced technologies. This study was motivated by the realization that established IT risk-management frameworks might provide an adequate defence for small- and medium-sized enterprises (SMEs), especially those actively adopting new technologies. We reviewed that a dynamic IT risk-management framework, updated to reflect emerging technological changes, would offer improved security and privacy for SMEs. To evaluate this, we conducted a systematic literature review spanning 2016 to 2021, focusing on IT risk-management research in various application areas. This study revealed that, while established frameworks like NIST have their benefits, they need to be better suited to the unique needs of SMEs due to their high degree of abstractness, vague guidelines, and lack of adaptability to technological advancements. The findings suggest a pressing need to evolve IT risk-management frameworks, particularly by incorporating advanced methods such as system dynamics, machine learning, and technoeconomic and sociotechnological models. These innovative approaches provide a more dynamic, responsive, and holistic approach to risk management, thereby significantly improving the IT security of SMEs. The study’s implications underscore the urgency of developing flexible, dynamic, and technology-informed IT risk-management strategies, offering novel insights into a more practical approach to IT risk management.
The Potential Role of Ammonia for Hydrogen Storage and Transport: A Critical Review of Challenges and Opportunities
Authors: Negro Viviana , Noussan Michel , Chiaramonti David
Published: August 2023

Hydrogen is being included in several decarbonization strategies as a potential contributor in some hard-to-abate applications. Among other challenges, hydrogen storage represents a critical aspect to be addressed, either for stationary storage or for transporting hydrogen over long distances. Ammonia is being proposed as a potential solution for hydrogen storage, as it allows storing hydrogen as a liquid chemical component at mild conditions. Nevertheless, the use of ammonia instead of pure hydrogen faces some challenges, including the health and environmental issues of handling ammonia and the competition with other markets, such as the fertilizer market. In addition, the technical and economic efficiency of single steps, such as ammonia production by means of the Haber–Bosch process, ammonia distribution and storage, and possibly the ammonia cracking process to hydrogen, affects the overall supply chain. The main purpose of this review paper is to shed light on the main aspects related to the use of ammonia as a hydrogen energy carrier, discussing technical, economic and environmental perspectives, with the aim of supporting the international debate on the potential role of ammonia in supporting the development of hydrogen pathways. The analysis also compares ammonia with alternative solutions for the long-distance transport of hydrogen, including liquefied hydrogen and other liquid organic carriers such as methanol.
Estimation of the Influence of the Coil Resistance on the Power and Efficiency of the WPT System
Authors: Stankiewicz Jacek Maciej
Published: August 2023

This paper presents the results of an analysis of a low-power Wireless Power Transfer (WPT) system. The system consists of periodically distributed planar spiral coils that form the transmitting and receiving planes. An analytical and numerical analysis of the WPT system, over the frequency range from 100 to 1000 kHz, was carried out. A simpler and faster solution is the proposed use of an equivalent circuit represented by a single WPT cell. The influence of coil resistance changes on the power and efficiency of the WPT system was studied. This was obtained by changing the diameter of the wire from which the coils were wound. In addition, the size of the coil, the number of turns, and the distance between the two planes have changed. After a detailed analysis, the results showed that the highest efficiency values were obtained for a wire diameter of 200 μm, which means the lowest coil resistance. However, the lowest efficiency values were obtained for the smallest wire diameter, i.e., 100 µm, which means the highest coil resistance. In this case, the efficiency decreased by more than 40%. Based on the calculation results, it was also shown that it was better to accept the skin effect (efficiency decreased below 7%) than to reduce the wire diameter to eliminate it.
Controls of the Sandbody Scale and Fault Throw on the Lithology and Composite Reservoir Formation in the Baoyunting Slope, East China Sea
Authors: Yan Sujie , Zhou Xinghai , Pu Renhai , Fan Changyu
Published: August 2023

Under the conditions of many faults, sandbodies, and hydrocarbon sources on the slopes of faulted basins where structural traps are scarce, only a few sandbodies are capable of forming hydrocarbon pools, while most sandbodies act as aquifers. This situation presents a challenge for predicting favorable hydrocarbon accumulation areas and understanding controlling factors. The Pinghu Formation reservoirs in the Baoyunting nose structure of the Xihu Sag in the East China Sea exemplify this characteristic. Among the 19 small-scale oil and gas reservoirs discovered in this area, 10 are faulted sandbody composite traps and 9 are lithological traps, while the majority of the remaining sand layers, especially the thick layers, act as aquifers, resulting in significantly lower accumulation probabilities compared to the adjacent northern and southern areas. We analyzed the relationship between the sandstone thickness and the amplitude through the 1-D forward modeling of wells and dissected the 3-D seismic event to obtain the planar distribution of a single sandbody. Further comprehensive research on fault sealing and kinetic reservoir formation processes suggests that the gas pool formation in this area is closely related to fault sealing and lateral oil and gas transport. A small fault-to-caprock ratio is beneficial for the sealing of mudstone caprocks, while a large fault-to-sand thickness ratio is beneficial for the lateral sealing of faults and the formation of fault–sand composite pools. The tidal microfacies sandbody has a small scale, poor lateral transport ability, and a low probability of gas reservoir formation. The barrier and delta front sandbodies have a large scale, good lateral transport, and a high probability of reservoir formation. Based on the above methods, favorable pool formation traps were identified in the area, and high-yield gas wells were drilled.
Replacement Scenarios of LPG Boilers with Air-to-Water Heat Pumps for a Production Manufacturing Site
Authors: Carella Alberta , Del Ferraro Luca , D’Orazio Annunziata
Published: August 2023

The replacement of LPG (liquefied petroleum gas) boilers with air-to-water heat pumps on an industrial site is proposed. The boilers are used to produce hot water for the heating of two workshops and for the manufacturing process (test benches). The substitution aims to improve the local air quality in terms of pollutant emissions. The energy benefits in terms of reduction of primary energy and CO2 emissions are analysed. The reduction in primary energy consumption varies between 51% and 64% for two different scenarios that are evaluated, respectively, under design and real operating conditions; the latter is based on the trend in heat loads and outdoor air temperatures recorded in a small town of central Italy in the year 2022. The results also show a decrease in CO2 production of between 58% and 68%. This replacement, carried out in a manufacturing context, represents a case study that may also be applied to industrial suburban areas of cities.
Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting
Authors: Li Binglin , Shao Yong , Lian Yufeng , Li Pai , Lei Qiang
Published: August 2023

With the increase in population and the progress of industrialization, the rational use of energy in heating systems has become a research topic for many scholars. The accurate prediction of heat load in heating systems provides us with a scientific solution. Due to the complexity and difficulty of heat load forecasting in heating systems, this paper proposes a short-term heat load forecasting method based on a Bayesian algorithm-optimized long- and short-term memory network (BO-LSTM). The moving average data smoothing method is used to eliminate noise from the data. Pearson’s correlation analysis is used to determine the inputs to the model. Finally, the outdoor temperature and heat load of the previous period are selected as inputs to the model. The root mean square error (RMSE) is used as the main evaluation index, and the mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2) are used as auxiliary evaluation indexes. It was found that the RMSE of the asynchronous length model decreased, proving the general practicability of the method. In conclusion, the proposed prediction method is simple and universal.
A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models
Authors: Bhansali Ashok , Narasimhulu Namala , Pérez de Prado Rocío , Divakarachari Parameshachari Bidare , Narayan Dayanand Lal
Published: August 2023

Today, methodologies based on learning models are utilized to generate precise conversion techniques for renewable sources. The methods based on Computational Intelligence (CI) are considered an effective way to generate renewable instruments. The energy-related complexities of developing such methods are dependent on the vastness of the data sets and number of parameters needed to be covered, both of which need to be carefully examined. The most recent and significant researchers in the field of learning-based approaches for renewable challenges are addressed in this article. There are several different Deep Learning (DL) and Machine Learning (ML) approaches that are utilized in solar, wind, hydro, and tidal energy sources. A new taxonomy is formed in the process of evaluating the effectiveness of the strategies that are described in the literature. This survey evaluates the advantages and the drawbacks of the existing methodologies and helps to find an effective approach to overcome the issues in the existing methods. In this study, various methods based on energy conversion systems in renewable source of energies like solar, wind, hydro power, and tidal energies are evaluated using ML and DL approaches.
The Emergence of Large-Scale Bioethanol Utilities: Accelerating Energy Transitions for Cooking
Authors: Osiolo Helen Hoka , Marwah Hanaan , Leach Matthew
Published: August 2023

Expansion in access to clean cooking in Sub-Saharan Africa remains well below the UN’s Sustainable Development Goal objectives. In particular, clean and modern forms of cooking have struggled to attract commercial funding at scale. The use of bioethanol in cooking is not new, but until recently, its application has been confined exclusively to small-scale projects. However, a new bioethanol cooking utility in Kenya has now reached mass-market adoption, serving more than 950,000 households with cooking fuel since its launch in late 2019. Its success was made possible by a significant investment in technology to facilitate safe, convenient, and affordable fuel distribution. It is funded by climate finance, which is based on bioethanol fuel replacing the charcoal normally used for cooking; a leading cause of African deforestation. This development is so recent that it has not been widely discussed in the academic literature. More broadly, the health, environmental, and economic impacts of bioethanol for cooking have not been systematically assembled in one place. The main aim of this study is to identify how KOKO Networks has managed to overcome the traditional barriers to scalability, achieving impacts with bioethanol for accelerating energy transitions for cooking. The results show that bioethanol for cooking supports 13 out of 17 SDGs and has significant positive impacts on health, the environment, and the wider economy. The affordability of bioethanol has been made possible because of KOKO Investments in high-tech electronic fuel dispensing machines and through the use of climate financing. KOKO relies both on local and imported fuel to offer reliability and security of supply, as well as to grow commercial bioethanol demand to support the growth of the local bioethanol industry. Bioethanol for cooking also suffers from unfavorable tax regimes. This is because historically, in many countries, ethanol has been imported for use in the beverage industry. In addition, an appropriate commercial supply chain and delivery model which boosts the scalability of business and offers customer convenience is essential. For these conditions to take place, an enabling policy environment is key.
A Parametric Study on the LDB Strength of Steel-Concrete Composite Beams
Authors: Rossi Alexandre , de Carvalho Adriano Silva , de Oliveira Vinicius Moura , de Souza Alex Sander Clemente , Martins Carlos Humberto
Published: August 2023

Lateral distortional buckling (LDB) is an instability phenomenon characteristic of steel-concrete composite beams (SCCB) that occurs in the presence of hogging moments in regions close to internal supports. The LDB behavior in SCCB is not yet fully understood. The procedures for determining the LDB strength are based on the classic lateral torsional buckling theory or on the inverted U-frame model. In addition, the standard procedures make use of the classic design curves of the SSRC (Structural Stability Research Council) and ECCS (European Convention for Constructional Steelwork) developed to analyze the stability behavior of steel elements. However, studies indicate that the use of the same empirical curves obtained for the analysis of steel elements leads to the conservative results of the LDB strength in SCCB. Therefore, this article aims to assess the LDB strength in SCCB through the development of post-buckling numerical analysis using the ABAQUS software. In the parametric study, four types of steel with different mechanical properties were analyzed. In addition, the I-section, the unrestrained length, and the reinforcement rate in the concrete slab were varied. The results showed the influence of the steel type on the LDB strength and deviations from the standard procedures. A small influence of the longitudinal reinforcement area variation was verified in the LDB strength in the FE analyses; however, this factor is significantly important in the standard procedures, causing considerable divergences. These results can provide a reference for future research and specification reviews.
Mycorrhizal Fungi Reclamation Promotes Stoichiometric Homeostasis of Re-Vegetation Types and Affects Soil Bacterial Function in Mining Subsidence of Northern Loess Plateau
Authors: Xiao Li , Bi Yinli , Wang Dongdong
Published: August 2023

Re-vegetation types and mycorrhizal fungi reclamation play a vital role in the improvement of soil quality in the mining subsidence of the northern Loess Plateau. However, the effects of re-vegetation types and mycorrhizal fungi reclamation on plant stoichiometric homeostasis, soil bacterial communities and functional characteristics are still not understood well but are vital for mining green construction. Based on the fact that mycorrhizal fungi reclamation has been implemented for more than 10 years (inoculation with arbuscular mycorrhizal fungi (AMF) and control), we examined five re-vegetation types with different C:N:P stoichiometry in the roots, leaves and calculated homeostasis. Meanwhile, second-generation sequencing technology was used to measure soil bacterial communities and functional characteristics to further reveal the relationships between soil factors and bacteria that drive plant stoichiometry and homeostasis in the biological reclamation area of coal mining subsidence. Our results indicated that plant N:P ratio in the leaves of all re-vegetation types was less than 14, with the highest ratio observed in A. fruticosa (nitrogen-fixing plants), showing that re-vegetation growth was limited by the availability of nitrogen. Only leaves in AMF-inoculated plants were categorized as ‘homeostatic’, while inoculation with AMF in both leaves and roots could alleviate nitrogen restriction and improve ecological stoichiometric homeostasis. The dominant phylum was Proteobacteria, followed by Actinobacteria, Acidobacteria, accounting for 69.92%–73.22% of all bacterial species and 82% with Chloroflexi. Soil copiotrophic community (Proteobacteria) in the AMF inoculation area was higher than those in the control area under all re-vegetation types, while the oligotrophic community (Acidobacteria) was lower than the control. Further analysis showed that soil TP, SOC, C:N and HD played vital roles in shifting the soil bacteria community. Soil stoichiometry and AMF affect microbial composition. These results indicated that the re-vegetation types and mycorrhizal fungi reclamation could shift bacterial homogeneity. Hence, our results expound that mycorrhizal fungi reclamation could optimize the ecological strategies of reclaimed vegetation, alleviate N-limitations in plants, improve endogenous stability and promote the ecological function of soil bacteria, which provided theoretical bases for further understanding and application of green restoration and sustainable development in the mining subsidence of the northern Loess Plateau.
Research on the Impact of Cooperative Membership on Forest Farmer Household Income and Assets—Case Study from Liaoning Herbal Medicine Planting Cooperatives, China
Authors: Wang Jingyu , Zhao Zhe , Gao Lei
Published: August 2023

Improving the income and assets of forest farmers is the basis for realizing the sustainable development of forestry. In this paper, we tested the impact of membership in herbal medicine planting cooperatives on forest farmer household income and assets using the propensity score matching (PSM) method and household surveys of the study area. The results showed that cooperative membership can greatly improve forest farmer household income and assets; the higher the accumulation of forest farmer household social capital and human capital, the more farmers were inclined to participate in cooperatives. Householders who were migrant workers were more likely to make the decision to participate in cooperatives compared with those without migrant work experiences. The results of ATT further verified the conclusion that cooperative membership can significantly improve income and assets, which increased by 7.04% and 4.19%, respectively. In addition, according to the survey, the current development of cooperatives in the forestry area experienced problems such as inconsistent quantitative and qualitative development, insufficient driving force, irregular operating mode, inaccurate policy support, and inadequate guidance mechanisms. This paper focused on innovating cooperation mechanisms, enriching joint forms, enhancing driving capacity, stimulating internal driving forces, strengthening system construction, improving standards, enhancing guidance services, and strengthening institutional guarantees. These recommendations have been put forward to guide policy for sustainable forest development.
Timber Structures of Florence Cathedral: Wood Species Identification, Technological Implications and Their Forest Origin
Authors: Macchioni Nicola , Degl’Innocenti Marco , Mannucci Francesca , Stefani Ilaria , Lazzeri Simona , Caciagli Samuele
Published: August 2023

The Cathedral of Florence is one of the largest churches in the world and is known for one of the most famous domes ever, which characterizes the skyline of the city. The dimensions of the building mean that the dimensions of the roof are equally large and so are the wooden structures that support it. The roof of the cathedral is organized on two levels: the roof of the large central nave and, at a lower level, those of the two lateral naves. The purpose of this paper is the identification of the wood species of which the structures are made. The sampling method of the 408 samples that have been identified is then described, the methods followed to reach a reliable identification and finally the results. The timbers most represented among the structural elements are those of silver fir, chestnut and elm. Other timbers are then found in the other components less directly linked to the main structural parts that make up the trusses. The paper then discusses the technological implications on the use of those woods within the wooden covering structures of the cathedral and the main sources of timber that the builders had available, in particular the Casentino forests that the Municipality of Florence had donated to the structure that managed the construction of the cathedral (Opera di Santa Maria del Fiore—OPA). OPA still exists today and is responsible for the maintenance of the cathedral and other annexed buildings.