bluuebunny/crossref_metadata_2025 huggingface dataset:
Prompt Learning with Structured Semantic Knowledge Makes Pre-Trained Language Models Better
Authors: Zheng Hai-Tao , Xie Zuotong , Liu Wenqiang , Huang Dongxiao , Wu Bei , Kim Hong-Gee
Published: July 2023

Pre-trained language models with structured semantic knowledge have demonstrated remarkable performance in a variety of downstream natural language processing tasks. The typical methods of integrating knowledge are designing different pre-training tasks and training from scratch, which requires high-end hardware, massive storage resources, and long computing times. Prompt learning is an effective approach to tuning language models for specific tasks, and it can also be used to infuse knowledge. However, most prompt learning methods accept one token as the answer, instead of multiple tokens. To tackle this problem, we propose the long-answer prompt learning method (KLAPrompt), with three different long-answer strategies, to incorporate semantic knowledge into pre-trained language models, and we compare the performance of these three strategies through experiments. We also explore the effectiveness of the KLAPrompt method in the medical field. Additionally, we generate a word sense prediction dataset (WSP) based on the Xinhua Dictionary and a disease and category prediction dataset (DCP) based on MedicalKG. Experimental results show that discrete answers with the answer space partitioning strategy achieve the best results, and introducing structured semantic information can consistently improve language modeling and downstream tasks.
Control of Permanent Magnet Synchronous Motors for Track Applications
Authors: Mercorelli Paolo
Published: July 2023

For a wide variety of motion control systems, the PMSM (Permanent Magnet Synchronous Motors) drive is among the best options. The PMSMs, for instance, are frequently used for motors, power tools, and robotics and are currently being explored for high-power uses, including industrial motors and vehicle propulsion. Additionally, it has industrial and commercial uses. The PMSM is renowned for its great efficiency, greater power density, exceptional dynamic performance, as well as limited power ripple. The objective of this paper is to review literature that is based on tracking problems through the control of permanent magnet synchronous motors in terms of their control and functionality, including fault detection and performance.
Formal Security Analysis of ECC-Based RFID in Logic of Events Theory
Authors: Xiao Meihua , Chen Quangang , Li Zehuan , Chen Yuqiong , Xu Ruihan
Published: July 2023

Radio frequency identification (RFID) is a crucial component of the Internet of Things (IoT), and RFID using elliptic curve Cryptography (ECC) is a public key cryptosystem authentication approach that tackles the problem of electronic tag data encryption in RFID systems. The commercialisation and large-scale deployment of RFID systems has raised a number of security-related issues that suggest the need for security protocols. Logic of events theory (LoET) is a formal method for constructing and reasoning about distributed systems and protocols involving security concepts. This paper proposes three event classes, Compute, Retrieve, and Generate, and related axioms and inference rules to formally abstract the ECC session key generation function and formally institute the authentication process of both parties, and the ex-tended LoET is used to analyse the security properties of ECC-based RFID security protocols. Under reasonable assumptions, an ECC-based RFID mutual authentication scheme is shown to satisfy the strong mutual authentication feature. It is shown that extended logic of events theory may be used to prove the security properties of this class of ECC-based RFID protocols.
Optimization of Protection Level of GBAS with Gaussian Mixture Model
Authors: Wang Yao , Zhao Jingbo , Hao Shuo , Hui Shenying , Yu Baoguo
Published: July 2023

The Gaussian mixture model (GMM) is commonly used to model the heavy tail of the ground-based augmentation system (GBAS) range error distribution. In practice, Gaussian over-bounding based on a GMM is used to over-bound the heavy tail of the ranging errors, but the GBAS protection levels (PLs) based on the Gaussian over-bounding tend to be overestimated. Based on the idea of solution separation and overcoming the shortcoming of its direct reference to GBAS, this paper analyses the constraint conditions and objective functions of the optimal protection level based on solution separation under a GMM distribution, and proposes that multi-hypothesis solution set classification can effectively reduce the computational complexity. At the same time, least squares optimization and dynamic allocation of integrity risk are used to further reduce the protection level. This paper verifies the validity of the parameters of the GMM based on actual airport GBAS data, performs simulation verification of the typical scenarios of CAT I and CAT II/IIIa global GBAS under the Beidou 3 constellation, and analyses the performance improvement effect under different solution set traversal depths. The results show that when the traversal depths of CAT I and CAT II/IIIa are 4 and 6, the vertical protection level component of the ground ranging error is reduced by 14% and the total vertical protection level is reduced by 10%.
CluMP: Clustered Markov Chain for Storage I/O Prefetch
Authors: Jung Sungmin , Lee Hyeonmyeong , Jo Heeseung
Published: July 2023

Due to advancements in CPU and storage technologies, the processing speed of tasks has been increasing. However, there has been a relative slowdown in the data transfer speeds between disks and memory. Consequently, the issue of I/O processing speed has become a significant concern in I/O-intensive tasks. This research paper proposes CluMP, which predicts the next block to be requested within a process using a clustered Markov chain. Compared to the simple read-ahead approach commonly used in Linux systems, CluMP can predict prefetching more accurately and requires less memory for the prediction process. CluMP demonstrated a maximum memory hit ratio improvement of 191.41% in the KVM workload compared to read-ahead, as well as a maximum improvement of 130.81% in the Linux kernel build workload. Additionally, CluMP provides the advantage of adaptability to user objectives and utilized workloads by incorporating several parameters, thereby allowing for optimal performance across various workload patterns.
Research on Synthesis of Multi-Layer Intelligent System for Optimal and Safe Control of Marine Autonomous Object
Authors: Koznowski Wojciech , Kula Krzysztof , Lazarowska Agnieszka , Lisowski Józef , Miller Anna , Rak Andrzej , Rybczak Monika , Mohamed-Seghir Mostefa , Tomera Mirosław
Published: July 2023

The article presents the synthesis of a multi-layer group control system for a marine autonomous surface vessel with the use of modern control theory methods. First, an evolutionary programming algorithm for determining the optimal route path was presented. Then the algorithms—dynamic programming with neural state constraints, ant colony, and neuro-phase safe control algorithms—were presented. LMI and predictive line-of-sight methods were used for optimal control. The direct control layer is implemented in multi-operations on the principle of switching. The results of the computer simulation of the algorithms were used to assess the quality control.
Anomaly Detection in 6G Networks Using Machine Learning Methods
Authors: Saeed Mamoon M. , Saeed Rashid A. , Abdelhaq Maha , Alsaqour Raed , Hasan Mohammad Kamrul , Mokhtar Rania A.
Published: July 2023

While the cloudification of networks with a micro-services-oriented design is a well-known feature of 5G, the 6G era of networks is closely related to intelligent network orchestration and management. Consequently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have a big part to play in the 6G paradigm that is being imagined. Future end-to-end automation of networks requires proactive threat detection, the use of clever mitigation strategies, and confirmation that 6G networks will be self-sustaining. To strengthen and consolidate the role of AI in safeguarding 6G networks, this article explores how AI may be employed in 6G security. In order to achieve this, a novel anomaly detection system for 6G networks (AD6GNs) based on ensemble learning (EL) for communication networks was redeveloped in this study. The first stage in the EL-ADCN process is pre-processing. The second stage is the feature selection approach. It applies the reimplemented hybrid approach using a comparison of the ensemble learning and feature selection random forest algorithms (CFS-RF). NB2015, CIC_IDS2017, NSL KDD, and CICDDOS2019 are the three datasets, each given a reduced dimensionality, and the top subset characteristic for each is determined separately. Hybrid EL techniques are used in the third step to find intrusions. The average voting methodology is employed as an aggregation method, and two classifiers—support vector machines (SVM) and random forests (RF)—are modified to be used as EL algorithms for bagging and adaboosting, respectively. Testing the concept of the last step involves employing classification forms that are binary and multi-class. The best experimental results were obtained by applying 30, 35, 40, and 40 features of the reimplemented system to the three datasets: NSL_KDD, UNSW_NB2015, CIC_IDS2017, and CICDDOS2019. For the NSL_KDD dataset, the accuracy was 99.5% with a false alarm rate of 0.0038; the accuracy was 99.9% for the UNSW_NB2015 dataset with a false alarm rate of 0.0076; and the accuracy was 99.8% for the CIC_IDS2017 dataset with a false alarm rate of 0.0009. However, the accuracy was 99.95426% for the CICDDOS2019 dataset, with a false alarm rate of 0.00113.
Fintech Agents: Technologies and Theories
Authors: Pal Anagh , Gopi Shreya , Lee Kwan Min
Published: July 2023

Many financial technology (fintech) applications have incorporated interactive computer agents to act as mediators between the user and the fintech system. This paper provides a comprehensive review of interactive fintech agents from technological and social science perspectives. First, we explain the general fintech landscape and define interactive fintech agents. Next, we review the major technologies involved in creating fintech: (1) artificial intelligence and machine learning, (2) big data, (3) cloud computing, and (4) blockchain; as well as the specific key technologies enabling the following aspects of interactive fintech agents: (1) intelligence, (2) understanding of users, and (3) manifestation as social actors. Following the technology review, we examine issues and theories related to human-fintech agent interaction in the following areas: (1) agents’ understanding of users, (2) agents’ manifestation as social actors (via embodiment, emotion, and personality), and (3) users’ social interaction with agents. Finally, we suggest directions for future research on fintech agents.
An Enhancement Method in Few-Shot Scenarios for Intrusion Detection in Smart Home Environments
Authors: Chen Yajun , Wang Junxiang , Yang Tao , Li Qinru , Nijhum Nahian Alom
Published: July 2023

Different devices in the smart home environment are subject to different levels of attack. Devices with lower attack frequencies confront difficulties in collecting attack data, which restricts the ability to train intrusion detection models. Therefore, this paper presents a novel method called EM-FEDE (enhancement method based on feature enhancement and data enhancement) to generate adequate training data for expanding few-shot datasets. Training intrusion detection models with an expanded dataset can enhance detection performance. Firstly, the EM-FEDE method adaptively extends the features by analyzing the historical intrusion detection records of smart homes, achieving format alignment of device data. Secondly, the EM-FEDE method performs data cleaning operations to reduce noise and redundancy and uses a random sampling mechanism to ensure the diversity of the few-shot data obtained by sampling. Finally, the processed sampling data is used as the input to the CWGAN, and the loss between the generated and real data is calculated using the Wasserstein distance. Based on this loss, the CWGAN is adjusted. Finally, the generator outputs effectively generated data. According to the experimental findings, the accuracy of J48, Random Forest, Bagging, PART, KStar, KNN, MLP, and CNN has been enhanced by 21.9%, 6.2%, 19.4%, 9.2%, 6.3%, 7%, 3.4%, and 5.9%, respectively, when compared to the original dataset, along with the optimal generation sample ratio of each algorithm. The experimental findings demonstrate the effectiveness of the EM-FEDE approach in completing sparse data.
Electricity Load Lost in the Largest Windstorms—Is the Fragility-Based Model up to the Task?
Authors: Jasiūnas Justinas , Láng-Ritter Ilona , Heikkinen Tatu , Lund Peter D.
Published: July 2023

Most existing models for estimating electric system impacts from windstorms tend to have detailed representation only for the electric or only for the meteorological system. As a result, there is little evidence on how models with detailed electric systems and realistic wind gust field representations would perform in different windstorm cases. This work explores the evidence for the ability of such a fragility-based model to generate realistic spatiotemporal lost load profiles for the most impactful windstorm cases in Finland. The literature review shows multiple driving factors for windstorm impacts that are difficult to assess analytically, and similarities between the most impactful windstorms. All the available interruption data for thirteen years were analyzed, with their grouping by individual storm and calm periods. The fixing of time distribution fits for these periods show most faults as being within the 20% uncertainty bounds of the severity-dependent distribution trendlines. The medium-voltage electricity grid impact model with national coverage was applied for the three most impactful and most recent windstorm cases, with the model calibrated for one case. The generated spatiotemporal lost load profiles in all cases recreate historic profiles within the similar error margins of approximately 20%.
A Backflow Power Suppression Strategy for Dual Active Bridge Converter Based on Improved Lagrange Method
Authors: Zhang Xinwen , Wang Canlong
Published: July 2023

Dual-active bridge (DAB) converters are receiving increasing attention from researchers as a critical part of the power transmission of energy routers. However, the DAB converter generates a large backflow power in conventional control mode, and when the load is mutated, its output voltage takes longer to return to the reference value accompanied by large fluctuations. To solve the above problems, a hybrid strategy is proposed in this paper to optimize the converter. The mathematical models of the transmitted power and the backflow power were firstly derived through in-depth analysis of the DAB converter under extended-phase-shift (EPS) modulation, and the suppression of the backflow power was performed according to the improved Lagrange method utilized in the obtained results. Moreover, considering the poor dynamic characteristics of DAB converters under PI control, according to the state space average model of output voltage in the paper, a model prediction control equation is established to improve the dynamic response of the converter by predicting the output voltage value at the next moment. The simulation results verify the effectiveness of the optimization strategy presented in the text.
Substitution of Fossil Coal with Hydrochar from Agricultural Waste in the Electric Arc Furnace Steel Industry: A Comprehensive Life Cycle Analysis
Authors: Cardarelli Alessandro , Barbanera Marco
Published: July 2023

The iron and steel industry remains one of the most energy-intensive activities with high CO2 emissions. Generally, the use of fossil coal as chemical energy in an electric arc furnace (EAF) makes up 40–70% of the total direct emissions in this steelmaking process. Therefore, substituting conventional fossil fuels with alternatives is an attractive option for reducing CO2 emissions. In this study, the environmental impacts of EAF-produced steel were comprehensively assessed using pulverized hydrochar as the charged and injected material as a replacement for fossil coal. An environmental analysis was performed based on the LCA methodology according to the framework of ISO 14044. This study evaluated two different outlines: the use of fossil coal and its replacement with hydrochar from the winemaking industry as a carbon source in the EAF steelmaking process. The environmental impacts from the manufacturing of the hydrochar were calculated using different scenarios, including novel industrial ways to use vinasse as a moisture source for the co-hydrothermal carbonization of vine pruning and exhausted grape marc (EGM). The environmental impacts per unit of steel were reported as a function of the ratio between the fixed carbon of the injected material and the material amount itself. The results highlight the sustainability of the hydrothermal carbonization process and the use of the hydrochar in EAF steelmaking. Moreover, the electricity mix used for the EAF process has significant relevance. The main outline of the results might assist decision-makers to determine which technological route is most likely to be effective in reducing future CO2 emissions from the iron and steel industry.
Impact of the Scan Rate on the Stability Window of an Electrical Double-Layer Capacitor
Authors: Cougnon Charles
Published: July 2023

Because of the intermittent nature of renewable energy sources, such as solar or wind power, energy storage is becoming strategic for securing the energy transition. In this context, energy storage technologies must become robust, secure, and efficient, so that they become attractive and competitive solutions. For these reasons, the stability of storage systems is a matter which must concern us. In the supercapacitor community, methodologies used to evaluate the stability window are widely discussed and debated, but the way it is impacted by the charging regime is rarely investigated. This question is even more important as the stability window is only valid for the current rate at which it was determined. In this study, the impact of the charging regime on the stability window was investigated both qualitatively and quantitatively by cyclic voltammetry. Results clearly demonstrate that the stability window reduces as the scan rate decreases. This is because degradation processes can be viewed as the result of irreversible electrochemical processes. In severe cases, this reduction in stability is such that it can be fatal for the supercapacitor if such a change in the charging regime has not been anticipated.
Development and Substantiation of Approaches to the Management of Sewage Sludge of Different Storage Periods
Authors: Tsybina Anna , Wünsch Christoph , Glushankova Irina , Arduanova Anna
Published: July 2023

A widespread method of sewage sludge disposal is still simple storage in sludge lagoons. Subsequent thermal utilization is hardly possible because sludge properties change over time and energy content is reduced. Use as a soil conditioner in agriculture or landscaping is usually not possible due to high heavy metal contents. This paper describes a method in which a 10-year-old accumulated sewage sludge can be utilized as technical soil by mixing it with pyrolized fresh sewage sludge. For this purpose, physicochemical and toxicological characteristics of sewage sludge of different storage periods were identified, processes of thermal destruction of sewage sludge analyzed, toxicological characteristics of solid products of thermal sludge treatment determined, and the possibility of using the sewage sludge–pyrolysate mixture as technical soil was assessed. Results show that the gross calorific value of fresh and one-year stored sewage sludge is with approx. 15,000 kJ/kg dry basis sufficient to produce pyrolysate autothermally. It is also shown that when the pyrolysis residue is mixed with fresh or 1-year old sewage sludge, heavy metals can be immobilized and thus the leaching of heavy metals significantly reduced by up to 75%. The method described can thus be a possible option for recycling accumulated sewage sludge.
The Operation Strategy of the MIDC Systems for Optimizing Renewable Energy Integration of Jeju Power System
Authors: Son Hyeokjin , Jang Gilsoo
Published: July 2023

The power system on Jeju Island currently faces challenges due to an imbalance between power supply and demand owing to increasing renewable energy (RE) generation. Consequently, the multi-infeed high-voltage direct-current (MIDC) systems are expected to play an important role in reducing RE curtailment and increasing RE penetration. However, despite the balance between power supply and demand, transmission line (TL) overloads on Jeju Island, which has a low permissible rating, may lead to RE curtailment. We propose an operational strategy for the MIDC systems to mitigate TL overloading and RE curtailment. This strategy was aimed at managing the possible congestion of TLs, maintaining a power supply–demand balance, and increasing the penetration level of RE. In this paper, we present a scheme for calculating the operating points of the MIDC systems and allocating RE curtailment using optimization with security constraints to mitigate TL overloading. The proposed optimization model simplifies solving the mathematical equations involved by employing a sensitivity factor called the power transfer distribution factor (PTDF), using which linear programming (LP) optimization can be performed. To verify the feasibility of the operational strategy, case studies are conducted using an actual Jeju Island power system with a power system analysis tool—Power System Simulator for Engineering (PSS/E). The optimization problem was solved using MATLAB.
Structural Behavior of L-Type Flange Joint with Various Flange Flatness Conditions
Authors: Tran Thanh-Tuan , Park Hongbae , Lee Daeyong
Published: July 2023

The L-type flange joint is widely used to attach steel tower segments to each other. However, tolerances on the flange surface flatness may occur during its fabrication, leading to a negative impact on the bolt stress distribution. This study evaluates the influences of the flange surface flatness on the behavior of L-type flange joints through numerical simulations. First, the finite element model of a 5 MW L-type flange joint is established, and its accuracy is verified based on comparison with an experimental test. Using the same loading conditions and material properties, the influences of geometrical imperfections (i.e., flange-sided gap, tower-sided gap) on the structural response are investigated. Furthermore, the impact of the flange gap opening length is reported. The results show that the flange-sided gap outperforms the tower-sided gap, resulting in reduced stress concentration in the bolt. In addition, the stresses in flange-sided gapping joints increase with an increase in the opening length.
The Energy Crisis—Looking at the Renewable Transition
Authors: Pakulska Teresa
Published: July 2023

The recently observed consequences of climate change, the COVID-19 pandemic and the global energy crisis caused by the conflict between Russia and Ukraine have induced reflections on the so-called ‘green’ energy transformation [...]
Driving and Energy Profiles of Urban Bus Routes Predicted for Operation with Battery Electric Buses
Authors: Czapla Zbigniew , Sierpiński Grzegorz
Published: July 2023

Battery electric buses are used for operation on urban bus routes. The main disadvantage of battery electric buses is their limited range that depends on energy consumption. This paper presents a new approach to the estimation of energy consumption on urban bus routes based on driving and energy profiles. The energy consumption results from the travel parameters along the bus route. The travel parameters are described by driving profiles. The determination of driving profiles is based on GPS location data recorded by a receiver on a bus. Location data are recorded at consecutive track points at a constant frequency. For each track point, the distance to the preceding track point is determined using the location data, and then the speed and acceleration are calculated. The analyzed bus route is divided into sections. For each section, travel parameters consisting of travel time, speed parameters, and acceleration parameters are determined. Using travel parameters, the energy consumption is estimated for individual sections and the entire bus route. The estimated energy consumption is described by energy profiles. Experimental results have been obtained for the selected urban bus route under various traffic conditions. For the assumed model of energy consumption, the energy consumed on the entire bus route is 1.8 KWh/km at off-peak hours and 2.1 KWh/km at peak hours. The driving and energy profiles describe the urban bus routes well and allow evaluation of the suitability of the bus route for operation with battery electric buses.
Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A Study
Authors: Chang Qifan , Fan Likun , Zheng Lihui , Yang Xumin , Fu Yun , Kan Zixuan , Pan Xiaoqing
Published: July 2023

In the L Area, big data techniques are employed to manage the principal controlling factors of coalbed methane (CBM) production, thereby regulating single-well output. Nonetheless, conventional data cleansing and the use of arbitrary thresholds may result in an overemphasis on certain controlling factors, compromising the design and feasibility of optimization schemes. This study introduces a novel approach that leverages raw data without data cleaning and eschews artificial threshold setting for controlling factor identification. The methodology supplements previously overlooked controlling factors, proposing a more pragmatic CBM production adjustment scheme. In addition to the initial five controlling factors, this approach incorporates three additional ones, namely, dynamic fluid level state, drainage velocity, and fracturing displacement. This study presents a practical application case study of the proposed approach, demonstrating its ability to reduce reservoir damage during the coal fracturing process and enhance output through seal adjustments. Utilizing the full spectrum of original data and minimizing human intervention thresholds enriches the information available for model training, thereby facilitating the development of a more efficacious model.
Estimating In-Situ R-Value of Highly Insulated Building Walls Based on the Measurement of Temperature and Heat Flux Inside the Wall
Authors: Choi Doo-Sung , Lee Ye-Ji , Moon Ji-Hoon , Kim Yong-Shik , Ko Myeong-Jin
Published: July 2023

Accurate and rapid in situ measurements of the thermal resistance (R-value) of building envelopes are necessary for assessing planned performance and identifying appropriate retrofitting strategies. Although there are several approaches for in situ R-value estimation, the average method of ISO 9869-1 based on the heat flow meter method is the most widely used. However, discrepancies between theoretical and in situ R-values are frequently reported in many studies that employ this method. This study aimed to investigate the cause of this discrepancy in estimating in situ R-values of highly insulated building walls using the average method of ISO 9869-1 by conducting long-term experiments. This study was made possible due to a specially constructed test wall wherein more sensors were installed than are required by the ISO 9869-1 standard. The findings showed that discrepancies between heat fluxes on the internal surface and heat passing through the wall is the main cause of the error in in situ R-value estimation. Measurement results from winter showed that deviation from the theoretical R-value was 9.12% for the average method and 0.6% for the extended average method, determined by additionally using the temperature and heat flux inside the wall.
Techniques of Fluidic Thrust Vectoring in Jet Engine Nozzles: A Review
Authors: Afridi Saadia , Khan Tariq Amin , Shah Syed Irtiza Ali , Shams Taimur Ali , Mohiuddin Khawar , Kukulka David John
Published: July 2023

Thrust vectoring innovations are demonstrated ideas that improve the projection of aerospace power with enhanced maneuverability, control effectiveness, survivability, performance, and stealth. Thrust vector control systems following a variety of concepts have been considered for modern aircraft and missiles to enhance their military performance. Short Take-off and Landing (STOL) and control effectiveness at lower aircraft speeds can be achieved by employing Fluidic Thrust Vectoring Control (FTVC). This paper summarizes a range of ideas for FTVC that have been designed and tested both computationally and experimentally to determine the thrust vectoring performance of supersonic propulsion system nozzles. The conventional method of thrust vectoring involves mechanical means to deflect the direction of flow of the exhaust gases, whereas the most recent method involves fluidic-based thrust vectoring techniques. Fluid-based thrust vectoring has the advantages of simplicity and low weight over mechanical-based thrust vectoring, which has complex geometry and adds extra weight to the aircraft. The fluidic vectoring control nozzles are divided into seven categories: shock vector, bypass shock vector, counterflow, co-flow, throat skewing, dual throat, and bypass dual throat nozzle control. This paper provides a summary of each fluidic thrust vectoring technique with its characteristics, design, classification, and different operational criteria developed to date and compares the intrinsic characteristics of each technique. Based on the present literature, it is concluded that among all the fluidic control techniques, the bypass dual-throat nozzle control can achieve better thrust vectoring performance with large vector angles and low thrust loss.
Direct Tilt Controller Design with Disturbance Compensation and Implementation for a Narrow Tilting Electric Vehicle
Authors: Karamuk Mustafa , Alankus Orhan Behic
Published: July 2023

Three-wheeled electric city vehicles are becoming popular because they have lower cost and enable motorcycle driving feeling with electric powertrain performance. These vehicles need a driver assistant system, also known as an active tilting stability controller, to provide a safe cornering manoeuvre. Active tilt control methods are direct tilt control (DTC), steering tilt control (STC) and a combination of these methods. In this study, DTC system design with a servo motor actuator with simulation and experimental results are presented. State feedback control with pole placement design has been improved with disturbance compensation control. This novel controller structure enhances the response of DTC and enables a faster-tilting response. Simulation results are given up to 10 m/s speed. Experimental results of the developed method are given up to 3.05 m/s (11 km/h) speed on a three-wheeled electric vehicle. The speed control loop of the servo motor drive unit (SMDU) stabilizes the DTC system. In the state of the art, a proportional derivative controller is commonly used as a tilt controller. By including the speed control loop of SMDU in the tilt control system, the use of the derivative term can be eliminated. The stability effect of the speed control loop is shown by MATLAB analysis, simulations in Simulink and experimental step response test as well.
Impact of Street Lighting Level on Floodlights
Authors: Wachta Henryk , Baran Krzysztof , Różowicz Sebastian
Published: July 2023

The article presents selected results of research related to the use of outdoor lighting, mainly street lighting, in the design of facility illumination. The indicated subject matter primarily concerns architectural structures that are located in urbanized city spaces and in the strict centers of old cities. It is in these areas that there is usually a significant saturation of historic secular and sacred buildings with significant tourist values. At the same time, the immediate surroundings of the structures are usually wrapped by a dense network of street lighting infrastructure. This illumination can be a major setback in the process of illumination planning. Therefore, it is necessary to take into account the extent of this impact on the planned illumination work, related to the selection, mounting, and direction of illumination equipment. This is related to the distance of the street luminaires from the facade, the height of their installation, the distance of the poles from each other, the power of the street luminaires, and the luminous flux distribution of the luminaires used. The purpose of the work undertaken was to analyze the extent to which outdoor lighting influences the planned illumination of an architectural structure and to explore the possibility of its potential use as a component of illumination. Analytical work was conducted at two levels of detail using advanced graphical computer applications. After general considerations and the derivation of conclusions, an example of illumination of a large sacral building was realized successfully using the element of street lighting as a component of illumination.
Enhanced Natural Regeneration Potential of Sessile Oak in Northern Hungary: Role of Artificially Increased Density of Insectivorous Birds
Authors: Eötvös Csaba Béla , Fürjes-Mikó Ágnes , Paulin Márton , Gáspár Csaba , Kárpáti Marcell , Hirka Anikó , Csóka György
Published: July 2023

Both artificial and natural regeneration of oaks strongly depend on the quantity and quality of the acorn crop, which show high year-to-year variation. The volume of the acorn crop is influenced by many factors including biotic and abiotic effects. The quality (i.e., germination ability, nutrient reserves) of acorns can be decreased by carpophagous insect and fungal pathogen damage or by inadequate weather conditions. Defoliating insects (caterpillars, sawfly larva, etc.) can significantly decrease the acorn crop as well. The most important predators of the defoliators are insectivorous birds during their nesting period. However, in the oak forests of the northern Hungarian mountains, there is a shortage of natural nesting holes. The main aim of our study was to artificially increase the number of the available nesting holes by nest boxes and to maximize the number of breeding insectivorous birds and register the direct (Lepidoptera densities) and indirect (acorn crop quantity and quality) effects of their presence. We found only slight effects on Lepidoptera densities, so we failed to demonstrate a direct effect in the relatively low time frame of our study. But, we could show the indirect positive effect of increased predation on the health state of the acorn crop, resulting in higher seedling densities, which may improve the natural regeneration potential of sessile oak. This result supports the outstanding importance of natural nesting holes in broadleaved forests.
Effect of Commercial Black Ink on the Optical Properties of P(St-MMA-AA) Microsphere Photonic Crystal Coating on Wood Surface
Authors: Su Yue , Liu Yi , Hu Jing , Zhao Yuchen
Published: July 2023

The mixed emulsion of P(St-MMA-AA) colloidal microspheres doped with black substances in three commercial inks (pen ink, wood color essence, propylene pigment) was coated on the wood surface. The dried colloidal microspheres self-assembled on the wood surface to form a three-dimensional photonic crystal coating. The influence of the coating on the optical properties of the wood surface was analyzed, and the influence of the type, concentration and drying temperature of the black substance on the structural color coating was explored. When three kinds of commercial black ink were mixed with colloidal microsphere emulsion to obtain photonic crystals on the wood surface, the transmitted light and the scattered light outside the photonic band gap were absorbed by black ink, which effectively reduced the light scattering inside the crystal and significantly enhanced the depth of the photonic crystal structure color. This study utilizes both pigment color and structural color, providing an environmentally friendly and novel method for colored coating of wood surfaces.
Estimation of Flexural Tensile Strength as a Function of Shear of Timber Structures
Authors: Arroyo Felipe N. , Borges Jéssica F. , Junior Wanderlei M. Pereira , Santos Herisson F. , Oliveira Ivanildo A. , Panzera Túlio H. , Christoforo André L. , Lahr Francisco A. R.
Published: July 2023

To avoid the intrinsic difficulties regarding the characterization of wood (i.e., different applications in different directions), various normative documents present the relationships between properties; the Brazilian standard is of particular interest in this regard, because Brazil has a huge diversity of tree species from tropical forests, usually used in construction. In view of this, this research aimed to propose a new list of properties to be inserted in future versions of the Brazilian standards in order to help identify the species shear strength. It is expected that there is a correlation between the characteristic values of flexural strength (fm,k) and shear strength in the direction parallel to the wood fibers (fv,0,k), leading to models that make it possible to estimate one of the properties if the other is known, which was the main objective of this research. After finding a strong correlation between the properties, various regression models were evaluated. It can be concluded that the linear model composed only by the angular coefficient (equation with only one variable) presented a determination coefficient of 76.45%, which shows the good precision achieved in the estimation of one of the two variables compared if the other is known. Additionally, an attempt was made to define which probability distribution represents the resistance data by applying maximum likelihood (MLE), concluding that there is little difference between the representation by the normal distribution and the generalized extreme value (GEV) distribution. Another approach was to define the undermining coefficient to ensure the reliability of the prediction equation by the experiment-based calibration methodology defined by Eurocode.
Mapping the Spatial Distribution of Aboveground Biomass in China’s Subtropical Forests Based on UAV LiDAR Data
Authors: Wang Ganxing , Li Shun , Huang Chao , He Guowei , Li Yang , Feng Jiayuan , Tang Fangran , Yan Pengbin , Qiu Lihong
Published: July 2023

Accurately estimating aboveground biomass (AGB) is crucial for assessing carbon storage in forest ecosystems. However, traditional field survey methods are time-consuming, and vegetation indices based on optical remote sensing are prone to saturation effects, potentially underestimating AGB in subtropical forests. To overcome these limitations, we propose an improved approach that combines three-dimensional (3D) forest structure data collected using unmanned aerial vehicle light detection and ranging (UAV LiDAR) technology with ground measurements to apply a binary allometric growth equation for estimating and mapping the spatial distribution of AGB in subtropical forests of China. Additionally, we analyze the influence of terrain factors such as elevation and slope on the distribution of forest biomass. Our results demonstrate a high accuracy in estimating tree height and diameter at breast height (DBH) using LiDAR data, with an R2 of 0.89 for tree height and 0.92 for DBH. In the study area, AGB ranges from 0.22 to 755.19 t/ha, with an average of 121.28 t/ha. High AGB values are mainly distributed in the western and central-southern parts of the study area, while low AGB values are concentrated in the northern and northeastern regions. Furthermore, we observe that AGB in the study area exhibits an increasing trend with altitude, reaching its peak at approximately 1650 m, followed by a gradual decline with further increase in altitude. Forest AGB gradually increases with slope, reaching its peak near 30°. However, AGB decreases within the 30–80° range as the slope increases. This study confirms the effectiveness of using UAV LiDAR for estimating and mapping the spatial distribution of AGB in complex terrains. This method can be widely applied in productivity, carbon sequestration, and biodiversity studies of subtropical forests.
Decline in Semi-Natural Grasslands and Changes in Value Perceptions in Japan’s Natural Parks
Authors: Yamaki Kazushige , Shibasaki Shigemitsu
Published: July 2023

The decline in semi-natural grasslands has resulted from reduced traditional use, leading to a marked decrease in their overall area. Despite being located within Japan’s core protected area system, the effectiveness of natural parks in conserving grasslands remains unclear. This study aimed to determine whether natural parks successfully prevent the decline in grasslands. By analyzing historical changes in value perceptions toward grasslands in natural parks, natural parks’ contribution to grassland conservation and the associated challenges were explored. The study focused on the Sengokuhara grassland in Fuji-Hakone-Izu National Park and incorporated previous studies on other grasslands. It revealed that grasslands have been valued for their scenic views and recreational potential since the establishment of the national park. As grassland scarcity increased nationwide, attention shifted toward rare plants found exclusively on grasslands, along with grasslands’ value as semi-natural landscapes. Consequently, natural-park-based grasslands have primarily been conserved based on these two values. However, conservation efforts within natural parks have mainly focused on regulating human activities, with limited measures to sustain nature, such as grasslands, through livelihoods. To enhance grassland conservation, it is essential to recognize their cultural value and develop mechanisms that ensure economic benefits are reinvested in conservation efforts.
Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
Authors: Jain Shushant Kumar , Bhatia Rinkoo , Shrivastava Neeraj , Salunke Sharad , Hashmi Mohammad Farukh , Bokde Neeraj Dhanraj
Published: July 2023

In the context of query-driven wireless sensor networks (WSNs), a unique scenario arises where sensor nodes are solicited by a base station, also known as a sink, based on specific areas of interest (AoIs). Upon receiving a query, designated sensor nodes are tasked with transmitting their data to the sink. However, the routing of these queries from the sink to the sensor nodes becomes intricate when the sink is mobile. The sink’s movement after issuing a query can potentially disrupt the performance of data delivery. To address these challenges, we have proposed an innovative approach called Query-driven Virtual Grid-based Routing Protocol (VGRQ), aiming to enhance energy efficiency and reduce data delivery delays. In VGRQ, we construct a grid consisting of square-shaped virtual cells, with the number of cells matching the count of sensor nodes. Each cell designates a specific node as the cell header (CH), and these CHs establish connections with each other to form a chain-like structure. This chain serves two primary purposes: sharing the mobile sink’s location information and facilitating the transmission of queries to the AoI as well as data to the sink. By employing the VGRQ approach, we seek to optimize the performance of query-driven WSNs. It enhances energy utilization and reduces data delivery delays. Additionally, VGRQ results in ≈10% and ≈27% improvement in energy consumption when compared with QRRP and QDVGDD, respectively.
The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges
Authors: Bandi Ajay , Adapa Pydi Venkata Satya Ramesh , Kuchi Yudu Eswar Vinay Pratap Kumar
Published: July 2023

Generative artificial intelligence (AI) has emerged as a powerful technology with numerous applications in various domains. There is a need to identify the requirements and evaluation metrics for generative AI models designed for specific tasks. The purpose of the research aims to investigate the fundamental aspects of generative AI systems, including their requirements, models, input–output formats, and evaluation metrics. The study addresses key research questions and presents comprehensive insights to guide researchers, developers, and practitioners in the field. Firstly, the requirements necessary for implementing generative AI systems are examined and categorized into three distinct categories: hardware, software, and user experience. Furthermore, the study explores the different types of generative AI models described in the literature by presenting a taxonomy based on architectural characteristics, such as variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, transformers, language models, normalizing flow models, and hybrid models. A comprehensive classification of input and output formats used in generative AI systems is also provided. Moreover, the research proposes a classification system based on output types and discusses commonly used evaluation metrics in generative AI. The findings contribute to advancements in the field, enabling researchers, developers, and practitioners to effectively implement and evaluate generative AI models for various applications. The significance of the research lies in understanding that generative AI system requirements are crucial for effective planning, design, and optimal performance. A taxonomy of models aids in selecting suitable options and driving advancements. Classifying input–output formats enables leveraging diverse formats for customized systems, while evaluation metrics establish standardized methods to assess model quality and performance.
The Low Ontogenetic Diet Diversity and Flexibility of the Pike-Perch, Sander lucioperca (Linnaeus, 1758) (Osteichthyes, Percidae): A Case Study
Authors: Alieva Aminat K. , Nasibulina Botagoz M. , Bakhshalizadeh Shima , Kurochkina Tatyana F. , Popov Nikolai N. , Barbol Bekzhan I. , Bănăduc Doru , Jussupbekova Nurgul M. , Kuanysheva Gulnur A. , Ali Attaala M.
Published: July 2023

This study reveals the diet of pike-perch from two habitats within the Tersko-Caspian region. The feeding habits of pike-perch in the Sulak and Terek rivers and Tersko-Caspian region were investigated based on 354 specimens sampled monthly from a commercial catch in 2019. Their diet consisted of different prey, with fish representing the most important prey group (up to 90%). Rutilus caspicus dominated with a frequency of occurrence of 20.5, and Aspius aspius, Perca fluviatilis, and Chondrostoma oxyrhynchum, had 13, 9.5, and 8.3%. In the earlier ages 1+ and 2+, zooplankton dominated, followed by mysids and chironomids, which continues into the older ages but at decreasing rates. Analysis of monthly variations in stomach fullness indicated that feeding intensity fluctuated in time, with the highest values in March–April, and lowest in September and November. It was found that the diet of pike-perch in the western Caspian changed considerably from season to season. They begin to actively feed after wintering and before, or even during, spawning period. The spectrum of the diet of Sander lucioperca includes more than 20 elements, mainly juveniles of commercially valuable fish. This fish is characterized by a relatively low ontogenetic diet diversity and flexibility, which makes it sensitive to environmental changes, including anthropogenically induced ones. This fish partially compensates for this disadvantage with its lifestyle, namely, its active foraging movements and migrations from fresh water to salt water and back, following its prey species.