bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Efficacy of Diet and Short-Term Probiotic Intervention on Depressive Symptoms in Patients after Bariatric Surgery: A Randomized Double-Blind Placebo Controlled Pilot Study
Authors: Komorniak Natalia , Kaczmarczyk Mariusz , Łoniewski Igor , Martynova-Van Kley Alexandra , Nalian Armen , Wroński Michał , Kaseja Krzysztof , Kowalewski Bartosz , Folwarski Marcin , Stachowska Ewa
Published: November 2023

(1) Background: studies have shown that some patients experience mental deterioration after bariatric surgery. (2) Methods: We examined whether the use of probiotics and improved eating habits can improve the mental health of people who suffered from mood disorders after bariatric surgery. We also analyzed patients’ mental states, eating habits and microbiota. (3) Results: Depressive symptoms were observed in 45% of 200 bariatric patients. After 5 weeks, we noted an improvement in patients’ mental functioning (reduction in BDI and HRSD), but it was not related to the probiotic used. The consumption of vegetables and whole grain cereals increased (DQI-I adequacy), the consumption of simple sugars and SFA decreased (moderation DQI-I), and the consumption of monounsaturated fatty acids increased it. In the feces of patients after RYGB, there was a significantly higher abundance of two members of the Muribaculaceae family, namely Veillonella and Roseburia, while those after SG had more Christensenellaceae R-7 group, Subdoligranulum, Oscillibacter, and UCG-005. (4) Conclusions: the noted differences in the composition of the gut microbiota (RYGB vs. SG) may be one of the determinants of the proper functioning of the gut–brain microbiota axis, although there is currently a need for further research into this topic using a larger group of patients and different probiotic doses.
Intermittent Fasting on Neurologic Diseases: Potential Role of Gut Microbiota
Authors: Guo Mingke , Wang Xuan , Li Yujuan , Luo Ailin , Zhao Yilin , Luo Xiaoxiao , Li Shiyong
Published: November 2023

As the global population ages, the prevalence of neurodegenerative diseases is surging. These disorders have a multifaceted pathogenesis, entwined with genetic and environmental factors. Emerging research underscores the profound influence of diet on the development and progression of health conditions. Intermittent fasting (IF), a dietary pattern that is increasingly embraced and recommended, has demonstrated potential in improving neurophysiological functions and mitigating pathological injuries with few adverse effects. Although the precise mechanisms of IF’s beneficial impact are not yet completely understood, gut microbiota and their metabolites are believed to be pivotal in mediating these effects. This review endeavors to thoroughly examine current studies on the shifts in gut microbiota and metabolite profiles prompted by IF, and their possible consequences for neural health. It also highlights the significance of dietary strategies as a clinical consideration for those with neurological conditions.
The Potential Role of Nutrition in Overtraining Syndrome: A Narrative Review
Authors: la Torre Maria Ester , Monda Antonietta , Messina Antonietta , de Stefano Maria Ida , Monda Vincenzo , Moscatelli Fiorenzo , Tafuri Francesco , Saraiello Emma , Latino Francesca , Monda Marcellino , Messina Giovanni , Polito Rita , Tafuri Domenico
Published: November 2023

Competition between athletes and an increase in sporting knowledge have greatly influenced training methods while increasing the number of them more and more. As a result, the number of athletes who have increased the number and intensity of their workouts while decreasing recovery times is rising. Positive overtraining could be considered a natural and fundamental process when the result is adaptation and improved performance; however, in the absence of adequate recovery, negative overtraining could occur, causing fatigue, maladaptation, and inertia. One of the earliest forms of fatigue is overreaching. It is considered to be an accumulation of training that leads to reduced sports performance, requiring days or weeks to recover. Overreaching, if followed by adequate recovery, can lead to an increase in athletic performance. Nonetheless, if overreaching becomes extreme, combined with additional stressors, it could lead to overtraining syndrome (OTS). OTS, caused by systemic inflammation, leads to central nervous system (CNS) effects, including depressed mood, further inflammation, central fatigue, and ultimately neurohormonal changes. There are therefore not only physiological, biochemical, and immunological but also psychological symptoms or markers that must be considered, independently or together, being intrinsically linked with overtraining, to fully understand OTS. However, to date, there are very few published studies that have analyzed how nutrition in its specific food aspects, if compromised during OTS, can be both etiology and consequence of the syndrome. To date, OTS has not yet been fully studied, and the topic needs further research. The purpose of this narrative review is therefore to study how a correct diet and nutrition can influence OTS in all its aspects, from prevention to treatment.
Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development
Authors: Danishuddin  , Jamal Mohammad Sarwar , Song Kyoung-Seob , Lee Keun-Woo , Kim Jong-Joo , Park Yeong-Min
Published: November 2023

PROteolysis TArgeting Chimera (PROTAC) is an emerging technology in chemical biology and drug discovery. This technique facilitates the complete removal of the target proteins that are “undruggable” or challenging to target through chemical molecules via the Ubiquitin–Proteasome System (UPS). PROTACs have been widely explored and outperformed not only in cancer but also in other diseases. During the past few decades, several academic institutes and pharma companies have poured more efforts into PROTAC-related technologies, setting the stage for several major degrader trial readouts in clinical phases. Despite their promising results, the formation of robust ternary orientation, off-target activity, poor permeability, and binding affinity are some of the limitations that hinder their development. Recent advancements in computational technologies have facilitated progress in the development of PROTACs. Researchers have been able to utilize these technologies to explore a wider range of E3 ligases and optimize linkers, thereby gaining a better understanding of the effectiveness and safety of PROTACs in clinical settings. In this review, we briefly explore the computational strategies reported to date for the formation of PROTAC components and discuss the key challenges and opportunities for further research in this area.
Monitoring and Mapping Winter Wheat Spring Frost Damage with MODIS Data and Statistical Data
Authors: Chen Di , Liu Buchun , Lei Tianjie , Yang Xiaojuan , Liu Yuan , Bai Wei , Han Rui , Bai Huiqing , Chang Naijie
Published: November 2023

Spring frost is an extreme temperature event that poses a significant threat to winter wheat production and consequently jeopardizes food security. In the context of climate change, the accelerated phenology of winter wheat due to global warming advances the frost-sensitive stage, thereby escalating the risk of spring frost damage. Present techniques for monitoring and assessing frost damage heavily rely on meteorological data, controlled field experiments and crop model simulations, which cannot accurately depict the actual disaster situation for winter wheat. In this study, we propose a novel method that utilizes remote sensing index and statistical data to ascertain the spatial distribution of spring frost damage to winter wheat and evaluate the extent of damage. This method was employed to monitor and assess the spring frost damage event that occurred in Shandong province from 3 to 7 April 2018. The result shows that beginning on 3 April, the daily minimum temperature in western Shandong Province dropped significantly (decreased by 17.93 °C), accompanied by precipitation. The daily minimum temperature reached the lowest on 7 April (−1.48 °C). The growth of winter wheat began to be inhibited on 3 April 2018, and this process persisted until 13 April. Subsequently, the impact of spring frost damage on winter wheat ceased and growth gradually resumed. The affected area of winter wheat spanned 545,000 mu with an accuracy rate of 89.72%. Severely afflicted areas are mainly located in the cities of Jining, Zaozhuang, Dezhou, Heze, Liaocheng, Jinan and Tai’an in western Shandong province, and the yield reduction rates were 5.27~12.02%. Our monitoring results were consistent with the distribution of county-level winter wheat yield in 2018 in Shandong province, the daily minimum temperature distribution during spring frost and severely afflicted areas reported by the news. This method proves effective in delineating the spatial distribution of agricultural disasters and monitoring the extent of disaster damage. Furthermore, it can provide reliable information of disaster area and geospatial location for the agricultural department, thereby aiding in disaster damage assessment and post-disaster replanting.
Phytochemical Profiling and Antioxidant Capacity of Traditional Plants, Northern Thailand
Authors: Jeeno Peerapong , Yadoung Sumed , Yana Pichamon , Hongsibsong Surat
Published: November 2023

Traditional plants have played a significant role in human culture and medicine throughout history. These plants have the capability to synthesize a diverse range of chemical compounds that serve essential biological functions. This study’s objective was to analyze the phytochemical composition of five traditional plants, namely Emilia sonchifolia, Chloranthus erectus, Caesalpinia mimosoides, Acacia concinna, and Tacca chantrieri, native to northern Thailand, using LC-QTOF/MS analysis and assess their potential bioactivity through various assays including DPPH radical scavenging activity, ABTS radical scavenging activity, ferric ion reducing antioxidant power, total phenolic compounds, and total flavonoid content. The findings revealed the presence of natural bioactive compounds in each plant extract, which exhibited pharmacological activity. Notably, Caesalpinia mimosoides displayed the highest antioxidant capacity across all plant extracts (IC50 in DPPH with the methanol extract was 0.03 and 898.18 mg AAE/100 g with the ethanol extract), along with elevated levels of total phenolic and flavonoid content, which showed the highest TFC at 46.79 µgRE/g in the methanol extract. In conclusion, traditional plants possess notable biological constituents and antioxidant properties, suggesting their potential for bioactive applications. Based on these findings, these indigenous plants can serve as a valuable resource in traditional medicine, offering the possibility of uncovering new products with similar capabilities and additional therapeutic attributes worthy of future exploration.
Synthesis and Characterization of Pressure-Sensitive Adhesives Based on a Naphthyl Curing Agent
Authors: Chen Junhua , Li Shiting , Wang Xuan , Fang Lili , Huang Dingding , Ke Lin , Chen Jinlian , Wang Qingwei , Zhang He , Wu Yinping , Zhu Dongyu , Li Chunsheng , Hao Xiangying
Published: November 2023

The incorporation of a naphthyl curing agent (NCA) can enhance the thermal stability of pressure-sensitive adhesives (PSAs). In this study, a PSA matrix was synthesized using a solution polymerization process and consisted of butyl acrylate, acrylic acid, and an ethyl acrylate within an acrylic copolymer. Benzoyl peroxide was used as an initiator during the synthesis. To facilitate the UV curing of the solvent-borne PSAs, glycidyl methacrylate was added to introduce unsaturated carbon double bonds. The resulting UV-curable acrylic PSA tapes exhibited longer holding times at high temperatures (150 °C) compared to uncross-linked PSA tapes, without leaving any residues on the substrate surface. The thermal stability of the PSA was further enhanced by adding more NCA and increasing the UV dosage. This may be attributed to the formation of cross-linking networks within the polymer matrix at higher doses. The researchers successfully balanced the adhesion performance and thermal stability by modifying the amount of NCA and UV radiation, despite the peel strength declining and the holding duration shortening. This research also investigated the effects of cross-linking density on gel content, molecular weight, glass transition temperature, and other properties of the PSAs.
Click Addition Reaction of Urethane–Acrylate Resin Using Octa(3-thiopropyl)silsesquioxane Derivatives as Cross-Linking Agents
Authors: Pakuła Daria , Sztorch Bogna , Przekop Robert E. , Marciniec Bogdan
Published: November 2023

In this work, new partially substituted derivatives of octa(3-thiopropyl)silsesquioxane (SSQ-8SH) were synthesized. The article compares the thiol-ene reaction using two methods: radical mechanism, thermally initiated (AIBN), and in the presence of a photoinitiator (DMPA). Both the crystalline and the oil forms of SSQ-8SH were functionalized. Olefins with nonpolar alkyl groups (hexene, octene, and octadecene) and vinyltrimethoxysilane, allyl glycidyl ether, allyl 2,2,3,3,4,4,5,5-octafluoropentylether, allyl methacrylate, and styrene were used in the reactions, allowing to obtain seven new derivatives. All compounds were characterized using spectroscopic (1H NMR and 29Si NMR) and spectrometric (MALDI-TOF-MS) methods. The influence of functional groups on the water contact angle value was determined. The functionalization of the compound led to a contact angle value above 95° (SSQ-4SH-4OD). Density measurements and thermogravimetric analysis (TGA) were carried out for all compounds. The highest onset temperature (357.4 °C) and temperature at the maximum mass loss rate (377.3 °C) were observed for SSQ-SH-4OD. The addition of alkyl groups significantly decreased the density of compounds with increasing chain length (1.198 g/cm3; 1.162 g/cm3; 1.095 g/cm3 for hexene, octene, and octadecene, respectively). Silsesquioxanes have potential applications in various materials, such as UV-curable resins, allowing to modify, for example, their surface properties. Modification of a commercial photocurable resin with selected derivatives was carried out to determine the impact on physicochemical properties (TGA, WCA).
Analysis of Factors Impacting CO2 Assisted Gravity Drainage in Oil Reservoirs with Bottom Water
Authors: Lu Hao , Xin Xiankang , Ye Jinxi , Yu Gaoming
Published: November 2023

In recent years, there has been significant focus on the issue of global carbon emissions. One of the most prominent areas of research in this regard is the use of carbon capture, utilization, and storage (CCUS) technology in the petrochemical industry. At present, the utilization of CO2 Assisted Gravity Drainage (CAGD) in oil reservoirs, particularly those containing bottom water, is considered to be in the early stages of exploration and development. In this study, a mechanistic model was built, and five key factors influencing CAGD were analyzed. These factors included the reservoir structure, CO2 injection site, initial formation pressure, reservoir thickness, and CO2 injection rate. Then, the applicable rules governing CAGD in oil reservoirs with bottom water were obtained. Finally, these rules were employed in an actual reservoir to optimize the injection-production parameters. The results of the influence factor analysis indicated that CAGD was more suitable for anticline structural reservoirs. The combined top-waist CO2 injection could fully utilize gravity differentiation in a short timeframe to expand the lateral sweep range of the CO2. CAGD was more effective when the reservoir pressure was greater than the minimum miscible pressure and the reservoir thickness was between 25–50 m. The generation of a secondary CO2 cap was favored when the CO2 injection rate was 35,000 m3/d. Results from A Oilfield applications indicated that, following the application of CAGD technology, A Oilfield experienced an increase in cumulative oil production of 15.76 × 104 t, a 10% reduction in water cut, and an amount of 82.15 × 106 m3 of CO2 that was sequestered in the subsurface. These findings can offer practical insights and guidance for the future development of CAGD techniques in similar reservoirs.
Industrial Robots in Mechanical Machining: Perspectives and Limitations
Authors: Makulavičius Mantas , Petkevičius Sigitas , Rožėnė Justė , Dzedzickis Andrius , Bučinskas Vytautas
Published: November 2023

Recently, the need to produce from soft materials or components in extra-large sizes has appeared, requiring special solutions that are affordable using industrial robots. Industrial robots are suitable for such tasks due to their flexibility, accuracy, and consistency in machining operations. However, robot implementation faces some limitations, such as a huge variety of materials and tools, low adaptability to environmental changes, flexibility issues, a complicated tool path preparation process, and challenges in quality control. Industrial robotics applications include cutting, milling, drilling, and grinding procedures on various materials, including metal, plastics, and wood. Advanced robotics technologies involve the latest advances in robotics, including integrating sophisticated control systems, sensors, data fusion techniques, and machine learning algorithms. These innovations enable robots to adapt better and interact with their environment, ultimately increasing their accuracy. The main focus of this study is to cover the most common industrial robotic machining processes and to identify how specific advanced technologies can improve their performance. In most of the studied literature, the primary research objective across all operations is to enhance the stiffness of the robotic arm’s structure. Some publications propose approaches for planning the robot’s posture or tool orientation. In contrast, others focus on optimizing machining parameters through the utilization of advanced control and computation, including machine learning methods with the integration of collected sensor data.
Analysis of the E-Region Low-Altitude Quasi-Periodic Event in Low-Latitudes of China
Authors: Qiao Lei , Chen Gang , Su Mingkun , Yan Chunxiao , Liu Erxiao , Wu Jun
Published: November 2023

The low-altitude quasi-periodic (LQP) E-region echoes observed with the upgraded Hainan COherent scatter Phased Array Radar (HCOPAR) located in Hainan Island of China (19.5°N, 109.1°E) are presented. With the interferometry technique, interferometry equations were developed to investigate the structures of the irregularities. The results show that the plasma structures of the LQP echoes with a negative slope mainly drifted southwestward almost horizontally, but they also drifted southeastward in some striations. According to the movement of the plasma structures, we found strong wind shear in the region in which the LQP echoes were produced. Meanwhile, the periodicity of the LQP echoes may have been related to the strength of the wind shear. It is proposed that the plasma structures were most likely produced by the Kelvin–Helmholtz instability (KHI).
Evaluating the Performance of Geographic Object-Based Image Analysis in Mapping Archaeological Landscapes Previously Occupied by Farming Communities: A Case of Shashi–Limpopo Confluence Area
Authors: Thabeng Olaotse Lokwalo , Adam Elhadi , Merlo Stefania
Published: November 2023

The use of pixel-based remote sensing techniques in archaeology is usually limited by spectral confusion between archaeological material and the surrounding environment because they rely on the spectral contrast between features. To deal with this problem, we investigated the possibility of using geographic object-based image analysis (GEOBIA) to predict archaeological and non-archaeological features. The chosen study area was previously occupied by farming communities and is characterised by natural soils (non-sites), vitrified dung, non-vitrified dung, and savannah woody vegetation. The study uses a three-stage GEOBIA that comprises (1) image object segmentation, (2) feature selection, and (3) object classification. The spectral mean of each band and the area extent of an object were selected as input variables for object classifications in support vector machines (SVM) and random forest (RF) classifiers. The results of this study have shown that GEOBIA approaches have the potential to map archaeological landscapes. The SVM and RF classifiers achieved high classification accuracies of 96.58% and 94.87%, respectively. Visual inspection of the classified images has demonstrated the importance of the aforementioned models in mapping archaeological and non-archaeological features because of their ability to manage the spectral confusion between non-sites and vitrified dung sites. In summary, the results have demonstrated that the GEOBIAs ability to incorporate spatial elements in the classification model ameliorates the chances of distinguishing materials with limited spectral differences.
Extraction of Roof Feature Lines Based on Geometric Constraints from Airborne LiDAR Data
Authors: Cai Zhan , Ma Hongchao , Zhang Liang
Published: November 2023

Airborne LiDAR (Light Detection and Ranging) is an active Earth observing system, which can directly acquire high-accuracy and dense building roof data. Thus, airborne LiDAR has become one of the mainstream source data for building detection and reconstruction. The emphasis for building reconstruction focuses on the accurate extraction of feature lines. Building roof feature lines generally include the internal and external feature lines. Efficient extraction of these feature lines can provide reliable and accurate information for constructing three-dimensional building models. Most related algorithms adopt intersecting the extracted planes fitted by the corresponding points. However, in these methods, the accuracy of feature lines mostly depends on the results of plane extraction. With the development of airborne LiDAR hardware, the point density is enough for accurate extraction of roof feature lines. Thus, after acquiring the results of building detection, this paper proposed a feature lines extraction strategy based on the geometric characteristics of the original airborne LiDAR data, tracking roof outlines, normal ridge lines, oblique ridge lines and valley lines successively. The final refined feature lines can be obtained by normalization. The experimental results showed that our methods can achieve several promising and reliable results with an accuracy of 0.291 m in the X direction, 0.295 m in the Y direction and 0.091 m in the H direction for outlines extraction. Further, the internal feature lines can be extracted with reliable visual effects using our method.
Ultrafast Laser-Excited Optical Emission of Xe under Loose-Focusing Conditions
Authors: Burger Miloš , Latty Kyle S. , Frigerio Leandro , Arnaud Thiago , Hartig Kyle C. , Jovanovic Igor
Published: November 2023

The optical filament-based radioxenon sensing can potentially overcome the constraints of conventional detection techniques that are relevant for nuclear security applications. This study investigates the spectral signatures of pure xenon (Xe) when excited by ultrafast laser filaments at near-atmosphericpressure and in short and loose-focusing conditions. The two focusing conditions lead to laser intensity differences of several orders of magnitude and different plasma transient behavior. The gaseous sample was excited at atmospheric pressure using ∼7 mJ pulses with a 35 fs pulse duration at 800 nm wavelength. The optical signatures were studied by time-resolved spectrometry and imaging in orthogonal light collection configurations in the ∼400 nm (VIS) and ∼800 nm (NIR) spectral regions. The most prominent spectral lines of atomic Xe are observable in both focusing conditions. An on-axis light collection from an atmospheric air–Xe plasma mixture demonstrates the potential of femtosecond filamentation for the remote sensing of noble gases.
Special Issue “Machine Learning Methods for Biomedical Data Analysis”
Authors: Caiafa Cesar F. , Sun Zhe , Tanaka Toshihisa , Marti-Puig Pere , Solé-Casals Jordi
Published: November 2023

Machine learning is an effective method for developing automatic algorithms for analysing sophisticated biomedical data [...]
From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution
Authors: Shi Shuqi , Liu Zongze , Deng Xiaofei , Chen Sifan , Song Dongran
Published: November 2023

Conventional wind speed sensors face difficulties in measuring wind speeds at multiple points, and related research on predicting rotor effective wind speed (REWS) is lacking. The utilization of a lidar device allows accurate REWS prediction, enabling advanced control technologies for wind turbines. With the lidar measurements, a data-driven prediction framework based on empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict the REWS. Thereby, the time series of lidar measurements are separated by the EMD, and the intrinsic mode functions (IMF) are obtained. The IMF sequences are categorized into high-, medium-, and low-frequency and residual groups, pass through the delay processing, and are respectively used to train four GRU networks. On this basis, the outputs of the four GRU networks are lumped via weighting factors that are optimized by an equilibrium optimizer (EO), obtaining the predicted REWS. Taking advantages of the measurement information and mechanism modeling knowledge, three EMD–GRU prediction schemes with different input combinations are presented. Finally, the proposed prediction schemes are verified and compared by detailed simulations on the BLADED model with four-beam lidar. The experimental results indicate that compared to the mechanism model, the mean absolute error corresponding to the EMD–GRU model is reduced by 49.18%, 53.43%, 52.10%, 65.95%, 48.18%, and 60.33% under six datasets, respectively. The proposed method could provide accurate REWS prediction in advanced prediction control for wind turbines.
Multi-Objective Seagull Optimization Algorithm with Deep Learning-Enabled Vulnerability Detection for Secure Cloud Environments
Authors: Aljebreen Mohammed , Alohali Manal Abdullah , Mahgoub Hany , Aljameel Sumayh S. , Alsumayt Albandari , Sayed Ahmed
Published: November 2023

Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks. The design of IDS using a machine learning (ML) technique comprises a series of methods that can learn patterns from data and forecast the outcomes consequently. In this background, the current study designs a novel multi-objective seagull optimization algorithm with a deep learning-enabled vulnerability detection (MOSOA-DLVD) technique to secure the cloud platform. The MOSOA-DLVD technique uses the feature selection (FS) method and hyperparameter tuning strategy to identify the presence of vulnerabilities or attacks in the cloud infrastructure. Primarily, the FS method is implemented using the MOSOA technique. Furthermore, the MOSOA-DLVD technique uses a deep belief network (DBN) method for intrusion detection and its classification. In order to improve the detection outcomes of the DBN algorithm, the sooty tern optimization algorithm (STOA) is applied for the hyperparameter tuning process. The performance of the proposed MOSOA-DLVD system was validated with extensive simulations upon a benchmark IDS dataset. The improved intrusion detection results of the MOSOA-DLVD approach with a maximum accuracy of 99.34% establish the proficiency of the model compared with recent methods.
Cross-Coupled Sliding Mode Synchronous Control for a Double Lifting Point Hydraulic Hoist
Authors: Sun Chungeng , Dong Xiangxiang , Li Jipeng
Published: November 2023

This paper proposes a sliding mode synchronous control approach to enhance the position synchronization performance and anti-interference capability of a double lifting point hydraulic hoist. Building upon the cross-coupling synchronous control method, a coupling sliding mode surface is formulated, incorporating the single-cylinder following error and double-cylinder synchronization error. Additionally, a sliding mode synchronous controller is devised to ensure the convergence of both the single-cylinder following and synchronization error. The hyperbolic tangent function is introduced to reduce the single-cylinder following error and the buffeting of the double-cylinder synchronization error curve under sliding mode synchronous control. The simulation results show that the synchronization accuracy of the sliding mode cross-coupling synchronization control in the initial stage of the system is 53.1% higher than that of the Proportional-Derivative (PD) cross-coupling synchronization, and the synchronization accuracy in the steady state of the system is improved by 90%. The designed synchronous controller has better performance under external disturbances.
The Design of a Circularly Polarized Antenna Array with Flat-Top Beam for an Electronic Toll Collection System
Authors: Xu Tianfan , Xu Mengchi , Cai Xiao
Published: November 2023

Electronic toll collection (ETC), known as a non-stop toll collection system which can automatically realize payment by setting the identification antenna at the entrance, is always suffering from information exchange interruption caused by beam switching. A circularly polarized sector beam antenna array operating at 5.8 GHz with flat-top coverage is proposed, based on the weighted constrained method of the maximum power transmission efficiency (WCMMPTE). By setting the test receiving antennas at the specific angles of the ETC antenna array to be designed, constraints on the received power are introduced to control the radiation pattern and obtain the optimized distribution of excitations for antenna elements. A 1-to-16 feeding network, based on the microstrip transmission line theory is designed to feed a 4 × 4 antenna array. Simulation results show that the half-power beamwidth covers an angular range of −30° to 30° while the axial ratio is below 3dB, which meets the ETC requirements. Furthermore, the gain fluctuation among the needed range of −30° to 30° is lower than 0.7 dB, which is suitable for the ETC system to achieve a stable signal strength and uninterrupted communication.
Development of an Intelligent Wind Erosion Monitoring System
Authors: Wang Linhu , Li Chengyu , Lin Jianhui , Ni Siwen
Published: November 2023

Wind erosion monitoring is an important method for measuring soil erosion and desertification. However, the current wind erosion monitoring equipment has the disadvantages of low automation and low measurement accuracy. In this work, an intelligent wind erosion monitoring system is developed, which can automatically collect and upload information on sand and the environment. The structure of the mechanical parts is designed and optimized to reduce the measurement error caused by the windblown sand impact on the sample collection to improve the stability of the system. A specific scheme for the precision calibration of the load cell is developed and implemented. The jitter parameters of the load cell were determined using the JY61 six-axis acceleration sensor, and then the optimal scheme to eliminate the jitter error was determined by comparing two neural network models in MATLAB 2021a software, and the precision calibration of the load cell was completed. As a result, the system has a reliable mechanical structure and hardware system and a perfect error compensation processing scheme. In a certain period, the system can be fully automatic with stable operation. The field operation test of this system can meet the design requirements and improve the measurement accuracy of windblown sand wells.
Visibility Estimation Based on Weakly Supervised Learning under Discrete Label Distribution
Authors: Yan Qing , Sun Tao , Zhang Jingjing , Xun Lina
Published: November 2023

This paper proposes an end-to-end neural network model that fully utilizes the characteristic of uneven fog distribution to estimate visibility in fog images. Firstly, we transform the original single labels into discrete label distributions and introduce discrete label distribution learning on top of the existing classification networks to learn the difference in visibility information among different regions of an image. Then, we employ the bilinear attention pooling module to find the farthest visible region of fog in the image, which is incorporated into an attention-based branch. Finally, we conduct a cascaded fusion of the features extracted from the attention-based branch and the base branch. Extensive experimental results on a real highway dataset and a publicly available synthetic road dataset confirm the effectiveness of the proposed method, which has low annotation requirements, good robustness, and broad application space.
Optical Sensors for Bacterial Detection
Authors: Guliy Olga I. , Karavaeva Olga A. , Smirnov Andrey V. , Eremin Sergei A. , Bunin Viktor D.
Published: November 2023

Analytical devices for bacterial detection are an integral part of modern laboratory medicine, as they permit the early diagnosis of diseases and their timely treatment. Therefore, special attention is directed to the development of and improvements in monitoring and diagnostic methods, including biosensor-based ones. A promising direction in the development of bacterial detection methods is optical sensor systems based on colorimetric and fluorescence techniques, the surface plasmon resonance, and the measurement of orientational effects. This review shows the detecting capabilities of these systems and the promise of electro-optical analysis for bacterial detection. It also discusses the advantages and disadvantages of optical sensor systems and the prospects for their further improvement.
Urban Facility Management Improving Livability through Smart Public Spaces in Smart Sustainable Cities
Authors: Abdelkarim Shimaa Basheir , Ahmad Ahmad Mohammad , Ferwati Salim , Naji Khalid
Published: November 2023

Public spaces play a significant role in improving social, recreational and cultural activities that are inclusive. However, smart public spaces are required to save time and costs, and to provide comfort to the public, ultimately enhancing livability. There are various user requirements and demands to respond to. This research aims to adopt intelligent technologies applied in public spaces to facilitate livability through urban facility management (UFM) involvement in improving livability. The establishment of intelligent systems in the architecture industry goes hand in hand with the growing advancement of science and technology in several aspects of human life. A fundamental key to the success of smart cities is the high quality of livability offered to their residents and visitors. The UFM can enable livability by providing data centers, access controls, integrated security systems, monitoring, smart waste, energy and water management in public spaces. This study focused on five elements of livability, including accessibility, functional infrastructure, interaction and social cohesion, amenity and participation, quality of life through safety and security and privacy. The research methodology adopts the activity theory (AT) as its underpinning theory for the qualitative data collection process (interviews and a workshop) from industry experts in two prominent smart cities in Qatar, Lusail and Msheireb, along with a literature review. This study’s results provide information on existing smart city and public space features, infrastructures and their impact on livability. The findings of this study support the development of the smart livable public spaces (SLiPS) framework. The framework is a baseline for improving livability in smart cities, which allows future updates based on existing ICT infrastructure, enabling new functions and the availability of novel technology applied by UFM. The outcome of livability leads to the achievement of smart city goals.
Fault Detection in a Single-Bus DC Microgrid Connected to EV/PV Systems and Hybrid Energy Storage Using the DMD-IF Method
Authors: Sistani Alireza , Hosseini Seyed Amir , Sadeghi Vahideh Sadat , Taheri Behrooz
Published: November 2023

Variations in fault currents, short times to clear the fault, and a lack of a natural current zero-crossing point are the most important challenges that DC microgrid protection faces. This challenge becomes more complicated with the presence of electric vehicles and energy storage systems due to their uncertainties. For this reason, in this paper, a new method for fault detection in DC microgrids with the presence of electric vehicles and energy storage systems is proposed. The new proposed method uses the combination of dynamic mode decomposition and instantaneous frequency for fault detection. In this method, first, a reference signal is made using the voltage and current signal sampled from the DC microgrid using the dynamic mode decomposition method. Next, in order to detect the fault, the instantaneous frequency value of the reference signal is calculated by the Hilbert transform. The simultaneous use of voltage and current signals reduces the transient effects of the control system on the proposed protection method. In order to measure voltage and current signals, only one intelligent electronic device unit is used in this paper. The proposed new method has been tested on a single-bus DC microgrid with the presence of electric vehicles and energy storage systems in MATLAB 2019b software. The results show that this method can detect all types of faults in DC microgrids, electric vehicles, and photovoltaics. Also, this method is immune to the uncertainties of the generation of distributed generation resources and the existence of noise distortions in the measured signals.
Water Resources Evaluation and Sustainability Considering Climate Change and Future Anthropic Demands in the Arequipa Region of Southern Peru
Authors: Quiroz Jonathan A. , Garcia-Chevesich Pablo A. , Martínez Gisella , Martínez Kattia , Tejada-Purizaca Teresa , Murray Kyle E. , McCray John E.
Published: November 2023

Climate change and increases in human activities are threatening water availability in the Arequipa Region (southern Peru). However, to date, there has not been a comprehensive inventory of surface water data or an investigation of current surface water conditions or forecasted future conditions resulting from increased anthropic demand or stresses from climate change. This study evaluates surface water resources management including storage, diversions, and conveyance in the Arequipa Region, while creating a tool for the evaluation of future scenarios in the five main watersheds of this arid region of southern Peru. State-of-the art, open-source modeling software was used. Water uses for each watershed were evaluated against predicted reservoir inflows and streamflows for different periods. In addition to the above, 12 climate change models and different shared socioeconomic pathways (SSP) were ensembled for the five watersheds. A semi-distributed approach and an innovative simulation splitting approach was used for each watershed, which allowed for different starting dates for the simulations using all available data obtained from different sources (government and private). Results indicate that the region is expected to have increased flows during the wet season and no significant changes during the dry season. Reservoir inflows are expected to increase up to 42 and 216% for the lowest and highest SSP evaluated, respectively. Similarly, the model projected streamflow increases up to 295 and 704%, respectively. Regarding yearly water availability and considering current and future demands for the watersheds under study, water deficits are not expected in the future if current reservoir storage can be maintained, though it is expected that reservoirs won’t be able to store predicted higher flows, so important volumes of water could be lost during the wet season to the ocean by natural drainage. Given the uncertainty of climate change projections, if future water sustainability is desired, storage and irrigation efficiencies should be improved and reservoir sedimentation should be evaluated.
Removal of Magnesium in Zinc Hydrometallurgical System via Freezing Crystallization: From Laboratory Experiments to Industrial Application
Authors: Jin Xin , Zhen Yong , Li Xingbin , Du Min , Luo Xingguo , Wei Chang , Deng Zhigan , Li Minting
Published: November 2023

Magnesium (Mg) is not only a typical impurity but also an important valuable metal in the zinc (Zn) hydrometallurgical process. This study proposed the use of freezing crystallization to recover Mg and reduce the Mg2+ concentration in waste electrolyte solutions, meeting the requirements of Zn hydrometallurgy. The experimental results indicated that the Mg2+ concentration decreased from 23.60 g/L to 14.28 g/L under optimal conditions at a temperature of 263.15 K, holding time of 90.00–120.00 min, H2SO4 concentration of 143.00 g/L, crystal seed addition of 50.00 g/L, and agitation speed of 300.00 rpm. The crystallization mother liquor was returned to the Zn hydrometallurgical process. The crystallization product was a mixture of MgSO4·7H2O and ZnSO4·7H2O with an aspect of 17. Notably, this method resulted in no discharge of waste gas, waste liquid, or waste residue. Additionally, during the industrial application process, the average removal efficiency of Mg2+ was 40.15%. The concentration of Mg2+ in waste electrolyte was reduced from 25.00–27.00 g/L to 13.00–15.00 g/L. These results indicated that the method effectively controlled the concentration of Mg2+ in the waste electrolyte and facilitated the recovery of Mg resources from Zn hydrometallurgy.
A Review of Ecosystem Services Based on Bibliometric Analysis: Progress, Challenges, and Future Directions
Authors: Li Xiaoyu , Gong Shudan , Shi Qingdong , Fang Yuan
Published: November 2023

Assessing the value of ecosystem services (ESV) can promote coordinated economic and ecological development. This study aimed to systematically review ecosystem services (ES) research history and research methodology and used the CiteSpace software to analyze 4063 papers in the field of service value in the ecosystem and visually analyze the developments in ES assessment; a conceptual framework combined with landscape genetics for evaluating ES was then constructed. The results showed that, first, the number of articles in the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) databases has been increasing annually. Moreover, Ecosystem Services, Sustainability, Ecological Indicators, Science of The Total Environment and Journal of Cleaner Production are the top five journals publishing the greatest number of studies. Second, ES research has seeped into life cycles and landscapes. Lastly, valuation of ES under the impacts of climate change and land use is the current research hotspot. The landscape genetics conceptual framework proposed in this study is a classification system based on the nature of ecological processes and can provide new ideas and methods for ES assessment.
Analysis of Flood Risk for Vulnerable People Using Assumed Flood Area Data Focused on Aged People and Infants
Authors: Morisaki Yuma , Fujiu Makoto , Takayama Junichi
Published: November 2023

During a large-scale disaster in Japan, vulnerable people, such as aged people, injured and sick people, infants, pregnant women, and foreign visitors to Japan, are most likely to be affected. This trend has not changed even in the case of floods where the process of disaster development is relatively moderate. However, in the case of flood disasters, the impact on vulnerable people can be minimized by evaluating the actual damage condition. The purpose of this study is to estimate the flood risk in the event of flooding for the elderly, the injured and sick, and infants, among those who need special care during disasters. The infant population was calculated using data from the national census maintained by Japan, and the aged and injured population was calculated using the National Health Insurance data, which are medical big data. Using these data, the regional distribution of the population was calculated, and then a spatial analysis was conducted with the data on the expected flood area in the event of flooding to estimate the exposed population. Through the analysis in this study, it was possible to estimate the flood risk per township by attribute, focusing on the aged, injured and sick, and infants. In Komatsu City, Ishikawa Prefecture, which was the subject of analysis in this study, the exposure of the elderly population in Mukaimotoori, Imae, Shirae, and Oki was high. In addition, the exposure of the infant population was high in Imae, Oki, Shirae, and Hitotsuhari. Town characteristics with a large exposed population were obtained, which is expected to be utilized for preliminary planning in the event of a flood.
Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective
Authors: Soygazi Fatih
Published: November 2023

Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are taught during AI courses. The separate explanation of these core research areas is common, but the emergence of federated learning has triggered their combined usage. This paper describes a practical scenario in the energy domain where these technologies can be used together to provide a sustainable energy solution for predicting wind turbine active power production. The projects in the AI course were assigned prior to the step-by-step learning of MASs and ML. These concepts were applied using a wind turbine energy dataset collected in Turkey to predict the power production of wind turbines. The observed performance improvements, achieved by applying various agent architectures and data partitioning scenarios, indicate that boosting methods such as LightGBM yield better results even when the settings are modified. Additionally, a questionnaire about the assignments was filled out by the student groups to assess the impact of learning MASs and ML through project-based education. The application of MASs and ML in a hybrid way proves valuable for learning core concepts related to AI education, as evidenced by feedback from students.