bluuebunny/crossref_metadata_2025 huggingface dataset:
Fuzzy System to Assess Dangerous Driving: A Multidisciplinary Approach
Authors: Ronquillo-Cana Carlos Javier , Pancardo Pablo , Silva Martha , Hernández-Nolasco José Adán , Garcia-Constantino Matias
Published: May 2022

Dangerous driving can cause accidents, injuries and loss of life. An efficient assessment helps to identify the absence or degree of dangerous driving to take the appropriate decisions while driving. Previous studies assess dangerous driving through two approaches: (i) using electronic devices or sensors that provide objective variables (acceleration, turns and speed), and (ii) analyzing responses to questionnaires from behavioral science that provide subjective variables (driving thoughts, opinions and perceptions from the driver). However, we believe that a holistic and more realistic assessment requires a combination of both types of variables. Therefore, we propose a three-phase fuzzy system with a multidisciplinary (computer science and behavioral sciences) approach that draws on the strengths of sensors embedded in smartphones and questionnaires to evaluate driver behavior and social desirability. Our proposal combines objective and subjective variables while mitigating the weaknesses of the disciplines used (sensor reading errors and lack of honesty from respondents, respectively). The methods used are of proven reliability in each discipline, and their outputs feed a combined fuzzy system used to handle the vagueness of the input variables, obtaining a personalized result for each driver. The results obtained using the proposed system in a real scenario were efficient at 84.21%, and were validated with mobility experts’ opinions. The presented fuzzy system can support intelligent transportation systems, driving safety, or personnel selection.
Application of Radar Solutions for the Purpose of Bird Tracking Systems Based on Video Observation
Authors: Krenc Ksawery , Gradolewski Dawid , Dziak Damian , Kawalec Adam
Published: May 2022

Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely implemented in small and medium-size airports, as well as on wind turbines is based on a stereo-vision. However, to provide long-term observations allowing the determination of the hot spots of birds’ activity and forecast future events, a robust tracking algorithm is required. The aim of this paper is to review tracking algorithms widely used in Radar Science and assess the possibilities of application of these algorithms for the purpose of tracking birds with a stereo-vision system. We performed a survey-of-related works and simulations determined five state-of-the art algorithms: Kalman Filter, Nearest-Neighbour, Joint-Probabilistic Data Association, and Interacting Multiple Model with the potential for implementation in a stereo-vision system. These algorithms have been implemented and simulated in the proposed case study
Multi-Step Hourly Power Consumption Forecasting in a Healthcare Building with Recurrent Neural Networks and Empirical Mode Decomposition
Authors: Fernández-Martínez Daniel , Jaramillo-Morán Miguel A.
Published: May 2022

Short-term forecasting of electric energy consumption has become a critical issue for companies selling and buying electricity because of the fluctuating and rising trend of its price. Forecasting tools based on Artificial Intelligence have proved to provide accurate and reliable prediction, especially Neural Networks, which have been widely used and have become one of the preferred ones. In this work, two of them, Long Short-Term Memories and Gated Recurrent Units, have been used along with a preprocessing algorithm, the Empirical Mode Decomposition, to make up a hybrid model to predict the following 24 hourly consumptions (a whole day ahead) of a hospital. Two different datasets have been used to forecast them: a univariate one in which only consumptions are used and a multivariate one in which other three variables (reactive consumption, temperature, and humidity) have been also used. The results achieved show that the best performances were obtained with the multivariate dataset. In this scenario, the hybrid models (neural network with preprocessing) clearly outperformed the simple ones (only the neural network). Both neural models provided similar performances in all cases. The best results (Mean Absolute Percentage Error: 3.51% and Root Mean Square Error: 55.06) were obtained with the Long Short-Term Memory with preprocessing with the multivariate dataset.
Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System
Authors: Gaballa Mohamed , Abbod Maysam , Aldallal Ammar
Published: May 2022

In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush–Kuhn–Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.
Super-Pixel Guided Low-Light Images Enhancement with Features Restoration
Authors: Liu Xiaoming , Yang Yan , Zhong Yuanhong , Xiong Dong , Huang Zhiyong
Published: May 2022

Dealing with low-light images is a challenging problem in the image processing field. A mature low-light enhancement technology will not only be conductive to human visual perception but also lay a solid foundation for the subsequent high-level tasks, such as target detection and image classification. In order to balance the visual effect of the image and the contribution of the subsequent task, this paper proposes utilizing shallow Convolutional Neural Networks (CNNs) as the priori image processing to restore the necessary image feature information, which is followed by super-pixel image segmentation to obtain image regions with similar colors and brightness and, finally, the Attentive Neural Processes (ANPs) network to find its local enhancement function on each super-pixel to further restore features and details. Through extensive experiments on the synthesized low-light image and the real low-light image, the experimental results of our algorithm reach 23.402, 0.920, and 2.2490 for Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Natural Image Quality Evaluator (NIQE), respectively. As demonstrated by the experiments on image Scale-Invariant Feature Transform (SIFT) feature detection and subsequent target detection, the results of our approach achieve excellent results in visual effect and image features.
Finite-Time Interactive Control of Robots with Multiple Interaction Modes
Authors: Yang Jiantao , Sun Tairen
Published: May 2022

This paper proposes a finite-time multi-modal robotic control strategy for physical human–robot interaction. The proposed multi-modal controller consists of a modified super-twisting-based finite-time control term that is designed in each interaction mode and a continuity-guaranteed control term. The finite-time control term guarantees finite-time achievement of the desired impedance dynamics in active interaction mode (AIM), makes the tracking error of the reference trajectory converge to zero in finite time in passive interaction mode (PIM), and also guarantees robotic motion stop in finite time in safety-stop mode (SSM). Meanwhile, the continuity-guaranteed control term guarantees control input continuity and steady interaction modes transition. The finite-time closed-loop control stability and the control effectiveness is validated by Lyapunov-based theoretical analysis and simulations on a robot manipulator.
Film-Thickness Identification Method and Lubrication Characteristic Experiment of Full-Size Water-Lubricated Stern Bearing under Offset Load
Authors: Li Jinjun , Ouyang Wu , Liu Qilin , Zhang Zhuo , Zhang Yan
Published: May 2022

Water-lubricated stern bearing (WSB) is a vital part of the ship propulsion-shaft system, and it is of great significance to monitor and analyze its lubrication status through film thickness data to improve the equipment operational reliability. In this paper, a full-size, large length-to-diameter ratio WSB experiment is carried out, and multi-sectional journal displacement data are collected under offset load. Accordingly, a bearing film-thickness identification model is established, which can identify the dynamic film thickness data in the circumferential direction of bearing section by limited measurement points. On this basis, the film thickness distribution of the full bearing is obtained by combining finite element (FE) simulation and particle swarm optimization (PSO) algorithm. The effect of different speeds on the distributed lubrication characteristics of WSB under offset load was systematically analyzed based on film thickness data. Results show that the maximum identification error of the bearing film-thickness identification model is less than 7%. The bearing lubrication state changes dynamically as the speed increases, and the hydrodynamic lubrication effect in the middle of the bearing is enhanced. The area of each lubrication sub-region varies nonlinearly. Research results are instructive for further determine the service life of the shaft system.
A Security Enhancement of the Precision Time Protocol Using a Trusted Supervisor Node
Authors: Alghamdi Waleed , Schukat Michael
Published: May 2022

The Precision Time Protocol (PTP) as described in IEEE 1588–2019 provides a sophisticated mechanism to achieve microsecond or even sub-microsecond synchronization of computer clocks in a well-designed and managed network, therefore meeting the needs of even the most time-sensitive industrial and financial applications. However, PTP is prone to many security threats that impact on a correct clock synchronization, leading to potentially devastating consequences. Here, the most vicious attacks are internal attacks, where a threat actor has full access to the infrastructure including any cryptographic keys used. This paper builds on existing research on the impact of internal attack strategies on PTP networks. It shows limitations of existing security approaches to tackle internal attacks and proposes a new security approach using a trusted supervisor node (TSN), in line with prong D as specified in IEEE 1588–2019. A TSN collects and analyzes delay and offset outputs of monitored slaves, as well as timestamps embedded in PTP synchronization messages, allowing it to detect abnormal patterns that point to an attack. The paper distinguishes between two types of TSN with different capabilities and proposes two different detection algorithms. Experiments show the ability of the proposed method to detect all internal PTP attacks, while outlining its limitations.
Validation of Instrumented Football Shoes to Measure On-Field Ground Reaction Forces
Authors: Karamanoukian Alexandre , Boucher Jean-Philippe , Labbé Romain , Vignais Nicolas
Published: May 2022

Ground reaction forces (GRF) have been widely studied in football to prevent injury. However, ambulatory tools are missing, posing methodological limitations. The purpose of this study was to assess the validity of an innovative football shoe measuring normal GRF (nGRF) directly on the field through instrumented studs. A laboratory-based experiment was first conducted to compare nGRF obtained with the instrumented shoe (IS) to vertical GRF (vGRF) obtained with force platform (FP) data, the gold standard to measure vGRF. To this aim, three subjects performed 50 steps and 18 counter-movement jumps (CMJs). Secondly, eleven subjects completed running sprints at different velocities on a football field, as well as CMJs, while wearing the IS. Good to excellent agreement was found between the vGRF parameters measured with the FP and the nGRF measured by the IS (ICC > 0.75 for 9 out of 11 parameters). Moreover, on-field nGRF patterns demonstrated a progressive and significant increase in relation with the running velocity (p < 0.001). This study demonstrated that the IS is a highly valid tool to assess vGRF patterns on a football field. This innovative way to measure vGRF in situ could give new insights to quantify training load and detect neuromuscular fatigue.
A Real-Time Monitoring Method for Civil Aircraft Take-Off and Landing Based on Synthetic Aperture Microwave Radiation Technology
Authors: Chen Houcai , Ge Junxiang , Kong Deqing , Zhao Zhenwei , Zhu Qinglin
Published: May 2022

It is important to monitor the take-off and landing of civil aircraft using passive detection methods. Due to the strict aircraft safety requirements and the electromagnetic environment around an airport, using too many active detection methods should be avoided. Using an aircraft’s microwave radiation signal detection is very advantageous because it does not actively emit signals and has a strong cloud penetration, suitable for all-weather observation. This paper introduces a synthetic aperture microwave radiation system for monitoring the take-off and landing of civil aircraft, which is characterized by real-time two-dimensional imaging, and the image refresh rate can reach 10 ms, which meets the high refresh rate requirements for aircraft imaging. Applicable system parameters and antenna array distribution scheme and imaging algorithm are given. Then the paper focuses on the error analysis and correction method of the system. The correction method is simple and fast, which avoids the disadvantage that the error needs to be corrected regularly in the laboratory environment, and is suitable for airport application. Finally, the simulation and experimental results show that this technology can be used for real-time monitoring of civil aircraft during take-off and landing, and it is a practical means to assisting landing.
Automatic Speech Recognition Method Based on Deep Learning Approaches for Uzbek Language
Authors: Mukhamadiyev Abdinabi , Khujayarov Ilyos , Djuraev Oybek , Cho Jinsoo
Published: May 2022

Communication has been an important aspect of human life, civilization, and globalization for thousands of years. Biometric analysis, education, security, healthcare, and smart cities are only a few examples of speech recognition applications. Most studies have mainly concentrated on English, Spanish, Japanese, or Chinese, disregarding other low-resource languages, such as Uzbek, leaving their analysis open. In this paper, we propose an End-To-End Deep Neural Network-Hidden Markov Model speech recognition model and a hybrid Connectionist Temporal Classification (CTC)-attention network for the Uzbek language and its dialects. The proposed approach reduces training time and improves speech recognition accuracy by effectively using CTC objective function in attention model training. We evaluated the linguistic and lay-native speaker performances on the Uzbek language dataset, which was collected as a part of this study. Experimental results show that the proposed model achieved a word error rate of 14.3% using 207 h of recordings as an Uzbek language training dataset.
Design and Analysis of a Hybrid Annular Radial Magnetorheological Damper for Semi-Active In-Wheel Motor Suspension
Authors: Munyaneza Olivier , Turabimana Pacifique , Oh Jong-Seok , Choi Seung-Bok , Sohn Jung Woo
Published: May 2022

In this study, a novel hybrid annular radial magnetorheological damper (HARMRD) is proposed to improve the ride comfort of an electric vehicle (EV) powered by an in-wheel motor (IWM). The model primarily comprises annular-radial ducts in series with permanent magnets. Mathematical models representing the governing motions are formulated, followed by finite element analysis of the HARMRD to investigate the distribution of the magnetic field density and intensity of the magnetorheological (MR) fluid in both the annular and radial ducts. The optimized model generates a damping force of 87.3–445.7 N at the off-state (zero input current) with the excitation velocity ranging between 0 and 0.25 m/s. By contrast, the generated damping force varies from 3386.4 N to 3753.9 N at an input current of 1.5 A with the same velocity range as the off state. The damping forces obtained using the proposed model are 31.4% and 19.2% higher for the off-field and on-field states, respectively, compared with those of the conventional annular radial MR damper. The efficiency of the proposed model is evaluated by adopting two different vehicles: a conventional vehicle powered by an engine and an EV powered by an IWM. The simulation results demonstrate that the proposed HARMRD along with the skyhook controller significantly improves both the ride comfort and road-holding capability for both types of vehicles.
A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections
Authors: Feng Dingzhong , He Shanyu , Zhou Zihao , Zhang Ye
Published: May 2022

In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add “Salt and pepper” noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.
Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment
Authors: Zhu Xiaoxun , Liu Baoping , Li Zhentao , Lin Jiawei , Gao Xiaoxia
Published: May 2022

CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal features with a fixed convolution kernel will affect the local feature perception and ultimately affect the learning effect and recognition accuracy. In order to solve this problem, the matching between the size of convolution kernel and the signal (rotation speed, sampling frequency) was optimized with the matching relation obtained. Through the study of this paper, the ability of extracting vibration features of CNN was improved, and the accuracy of vibration state recognition was finally improved to 98%.
LEOD-Net: Learning Line-Encoded Bounding Boxes for Real-Time Object Detection
Authors: Ibrahem Hatem , Salem Ahmed , Kang Hyun-Soo
Published: May 2022

This paper proposes a learnable line encoding technique for bounding boxes commonly used in the object detection task. A bounding box is simply encoded using two main points: the top-left corner and the bottom-right corner of the bounding box; then, a lightweight convolutional neural network (CNN) is employed to learn the lines and propose high-resolution line masks for each category of classes using a pixel-shuffle operation. Post-processing is applied to the predicted line masks to filtrate them and estimate clear lines based on a progressive probabilistic Hough transform. The proposed method was trained and evaluated on two common object detection benchmarks: Pascal VOC2007 and MS-COCO2017. The proposed model attains high mean average precision (mAP) values (78.8% for VOC2007 and 48.1% for COCO2017) while processing each frame in a few milliseconds (37 ms for PASCAL VOC and 47 ms for COCO). The strength of the proposed method lies in its simplicity and ease of implementation unlike the recent state-of-the-art methods in object detection, which include complex processing pipelines.
An Anti-UAV Long-Term Tracking Method with Hybrid Attention Mechanism and Hierarchical Discriminator
Authors: Cheng Feng , Liang Zhibo , Peng Gaoliang , Liu Shaohui , Li Sijue , Ji Mengyu
Published: May 2022

To prevent unmanned aerial vehicles (UAVs) from threatening public security, anti-UAV object tracking has become a critical issue in industrial and military applications. However, tracking UAV objects stably is still a challenging issue because the scenarios are complicated and the targets are generally small. In this article, a novel long-term tracking architecture composed of a Siamese network and re-detection (SiamAD) is proposed to efficiently locate UAV targets in diverse surroundings. Specifically, a new hybrid attention mechanism module is exploited to conduct more discriminative feature representation and is incorporated into a Siamese network. At the same time, the attention-based Siamese network fuses multilevel features for accurately tracking the target. We further introduce a hierarchical discriminator for checking the reliability of targeting, and a discriminator-based redetection network is utilized for correcting tracking failures. To effectively catch up with the appearance changes of UAVs, a template updating strategy is developed in long-term tracking tasks. Our model surpasses many state-of-the-art models on the anti-UAV benchmark. In particular, the proposed method can achieve 13.7% and 16.5% improvements in success rate and precision rate, respectively, compared with the strong baseline SiamRPN++.
A Coordinated Vehicle–Drone Arc Routing Approach Based on Improved Adaptive Large Neighborhood Search
Authors: Wu Guohua , Zhao Kexin , Cheng Jiaqi , Ma Manhao
Published: May 2022

Through urban traffic patrols, problems such as traffic congestion and accidents can be found and dealt with in time to maintain the stability of the urban traffic system. The most common way to patrol is using ground vehicles, which may be inflexible and inefficient. The vehicle–drone coordination maximizes utilizing the flexibility of drones and addresses their limited battery capacity issue. This paper studied a vehicle–drone arc routing problem (VD-ARP), consisting of one vehicle and multiple drones. Considering the coordination mode and constraints of the vehicle–drone system, a mathematical model of VD-ARP that minimized the total patrol time was constructed. To solve this problem, an improved, adaptive, large neighborhood search algorithm (IALNS) was proposed. First, the initial route planning scheme was generated by the heuristic rule of “Drone-First, Vehicle-Then”. Then, several problem-based neighborhood search strategies were embedded into the improved, adaptive, large neighborhood search framework to improve the quality of the solution. The superiority of IALNS is verified by numerical experiments on instances with different scales. Several critical factors were tested to determine the effects of coordinated traffic patrol; an example based on a real road network verifies the feasibility and applicability of the algorithm.
Prediction of Overall Energy Consumption of Data Centers in Different Locations
Authors: Zhang Yiliu , Liu Jie
Published: May 2022

The use of big data leads to higher demands for hyperscale data centers (HDCs) in terms of the scale and quantity required for data storage and processing. Before the construction of an HDC, it is necessary to comprehensively analyze the economic budget according to the energy requirements and potential energy cost. We propose a global energy consumption prediction framework based on the power usage effectiveness (PUE) calculation that considers all heat sources and power consumption. The framework integrates physical models and a statistical framework that combines IT equipment energy consumption and data center energy consuming predictions. Furthermore, the framework provides a method to calculate the carbon emissions and electricity cost of the data center. Using hourly meteorological data as climate parameters, combined with a limited range of energy parameters, the annual PUE values of 60 regions were estimated, and a further analysis of the Carbon Usage Effectiveness (CUE) and electricity costs in China was conducted as an example. Based on experimental validation and an evaluation of real-time data, our framework can predict the overall energy consumption of HDCs effectively, filling a gap in HDC research in the Asia-Pacific region and providing a basis for HDC feasibility analysis.
Design of Lidar Data Acquisition and Control System in High Repetition Rate and Photon-Counting Mode: Providing Testing for Space-Borne Lidar
Authors: Cheng Liangliang , Xie Chenbo , Zhao Ming , Li Lu , Yang Hao , Fang Zhiyuan , Chen Jianfeng , Liu Dong , Wang Yingjian
Published: May 2022

For ground-based lidars in atmospheric observation, their data acquisition unit and control unit usually work independently. They usually require the cooperation of large-volume, high-power-consumption Industrial Personal Computer (IPC). However, the space-borne lidar has high requirements on the stability and integration of the acquisition control system. In this paper, a new data acquisition and lidar control system (DALCS) was developed based on System-on-Chip Field-Programmable Gate Array (SoC FPGA) technology. It can be used in lidar systems with high repetition rate and photon-counting mode and has functions such as data storage, laser control, automatic collimation, wireless communication, and fault self-test. DALCS has two working modes: in online mode, the echo data collected by DALCS are transmitted to the computer for display in real-time and then stored with the current time as the file name; in offline mode, the data are stored in local non-volatile memory, which can be read remotely and can work autonomously when there is no IPC. The test results showed that in the frequency range of 0–70 M, the counting linearity of DALCS reached 0.9999, and the maximum relative error between the DALCS card and the standard signal source was 0.211%. The comparison results showed that the correlation coefficient between DALCS and MCS-PCI was as high as 0.99768. The DALCS was placed in a self-developed lidar sensor system for continuous observation, and the system worked stably under different weather conditions. The range-squared-corrected signal profiles obtained from the observations reflect the spatial and temporal distribution characteristics of aerosols and clouds well. This provides scheme verification and experimental support for the development of space-borne lidar data acquisition and control system.
Muted Voices: The Underrepresentation of Women in COVID-19 News in Portugal
Authors: Araújo Rita , Lopes Felisbela , Magalhães Olga , Cerqueira Carla
Published: May 2022

During the COVID-19 pandemic, the Portuguese media seemed to contribute to the symbolic annihilation of women. In spite of the fact that women play leading political roles as the Minister of Health and the Directorate-General of Health, women were almost mute in the COVID-19 news that was published in the Portuguese daily national press. In a sample of more than 6000 news sources, women account for less than 20% of them. Their lack of visibility in the news deepens the existing asymmetries of gender and amplifies the glass ceiling. The aim of this study was to analyze the media coverage of COVID-19 through a content analysis of the news that was published in two Portuguese daily newspapers with different editorial lines. Our period of analysis corresponds to the emergency-state periods (18 March to 2 May 2020; 9 November to 23 December 2020; 15 January to 28 February 2021).
Equations for Calculating the Thermal Conductivity of Capillary-Porous Materials with over Sorption Moisture Content
Authors: Alsabry Abdrahman , Backiel-Brzozowska Beata , Nikitsin Vadzim I. , Nikitsin Serafim K.
Published: May 2022

This article is the result of the authors’ work on the method of calculating the effective thermal conductivity of moist capillary-porous materials used in wall partitions. The proposed methodology was developed based on the theory of generalized conductivity and geometric modeling of the structure. Materials are considered as heterogeneous ternary systems consisting of a solid skeleton, gas and liquid, and all components are simultaneously taken into account in the calculation. In this work, additional equations are constructed that allow calculation of the effective thermal conductivity of capillary-porous materials with over sorption moisture, thus extending the scope of application of the method to the entire possible range of changes in moisture content. The details of calculating the thermal conductivity of moist capillary-porous materials are demonstrated using experimental data for wall ceramics samples. It is shown that the proposed equations are capable of predicting the thermal conductivity of moist capillary-porous materials with sufficient accuracy. Knowledge of the thermal conductivity of materials in conditions of their actual moisture is fundamental in the sustainable design of new buildings as well as thermo-renovation and dump protection of historic ones.
Impact of ICTs on Innovation and Performance of Firms: Do Start-ups, Regional Proximity and Skills Matter?
Authors: Alam Khorshed , Ali Mohammad Afshar , Erdiaw-Kwasie Michael , Shahiduzzaman Md , Velayutham Eswaran , Murray Peter A. , Wiesner Retha
Published: May 2022

Managers need to better understand how information and communication technologies (ICTs) lead to informed decisions about the investment and advantages of such technologies. However, at best, the empirical evidence on the business value of technology is mixed in relation to small firms. A total of 43 firms satisfied the study’s definition of start-ups. The final survey included 54 questions on access to and use of ICTs, innovation, firm characteristics, as well as the participants’ demographics, of which 11 factors were analyzed as part of this study. We found compelling evidence to support the positive effects of ICTs on firm-level innovation and performance based on the primary survey data of 270 small and medium enterprises (SMEs) operating in an Australian regional area. Moreover, we found that ICT strategies and skills are important factors that drive innovation and the overall performance of SMEs. In addition, various conditions, such as an agile workplace culture and international trade, can help firms improve their performance. Young businesses, particularly start-ups with ICT skills, show an improved innovation capability. However, remoteness appears to influence innovation negatively for nascent firms. Thus, managers should focus on improving ICT skills, strategies, and networking that help facilitate tangible ICT investments to foster innovation and growth.
A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning
Authors: Delgoshaei Parastoo , Heidarinejad Mohammad , Austin Mark A.
Published: May 2022

Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems.
Water Resources Management in Mexico, Chile and Brazil: Comparative Analysis of Their Progress on SDG 6.5.1 and the Role of Governance
Authors: Tinoco Clara , Julio Natalia , Meirelles Bruno , Pineda Raúl , Figueroa Ricardo , Urrutia Roberto , Parra Óscar
Published: May 2022

Integrated Water Resources Management (IWRM) is globally regarded as a suitable alternative to adapt to the effects of Climate Change. Chile, México, and Brazil are developing countries that are highly vulnerable to climate change and have different water management models, thus presenting the strengths and challenges for IWRM compliance. Our analysis aims to compare advances in terms of the achievement of SDG 6.5.1 in these countries, in relation to the degree of implementation of IWRM and their correlation to the OECD principles on Water Governance. We used IWRM Data Portal, UNEP-DHI, including 33 indicators, and we used the proposal of Bertule et al., (2018) to analyze the relationship among IWRM and governance. Our findings indicate that the Chilean model has low social involvement in water management due to a market-based water governance system with weak state regulation and lack of legal instruments to implement watershed management programs. In Mexico and Brazil, there is greater citizen involvement in watershed organizations, and ecosystems and aquifers have been incorporated to aid with water management. These three countries show a low performance of gender indicators, deficient financing, and information in terms of decision-making, and a lack of effective mechanisms for social participation, which are key aspects of governance.
Foliar Application of Macro- and Micronutrients Improves the Productivity, Economic Returns, and Resource-Use Efficiency of Soybean in a Semiarid Climate
Authors: Dass Anchal , Rajanna Gandhamanagenahalli A. , Babu Subhash , Lal Sanjay K. , Choudhary Anil K. , Singh Raj , Rathore Sanjay Singh , Kaur Ramanjit , Dhar Shiva , Singh Teekam , Raj Rishi , Shekhawat Kapila , Singh Chandu , Kumar Bipin
Published: May 2022

Inadequate nutrient management is one of the major challenges for sustainable soybean production in semi-arid climatic conditions. Hence, a 3-year (2015–2017) field experiment was conducted to assess the effect of foliar application of macro- and micronutrients on the growth, productivity, and profitability of soybean. Eight foliar nutrient sprays at the pod initiation stage—water spray (WS), 2% urea solution, 2% di-ammonium phosphate solution (DAP2%), 0.5% muriate of potash solution (MOP0.5%), 2% solution of 19:19:19 nitrogen phosphorus and potassium (NPK2%), and a 0.5% solution each of molybdenum (Mo0.5%), boron (B0.5%), chelated-zinc (Zn 0.5%) and no-foliar nutrition (NFN)—were compared with a basal-applied recommended dose of fertilizers (RDF: 30 kg N, 75 kg P, and 40 kg K ha−1) in a randomized block design (RBD), replicated three times. Foliar-applied chelated Zn@0.5% (Zn0.5%) at the pod initiation stage resulted in more pods per plants. In addition to Zn0.5%, urea2%, NPK2%, and B0.5% significantly improved the pods per plant over treatment by no-foliar nutrition (NFN). The RDF-supplied soybean subsequently sprayed with Zn0.5% produced the highest seed yield, which was 18.5–37.8% higher than that of NFN treatment Yield improvement due to the application of B0.5%, DAP2%, and urea2% varied between 19.2–23.7, 16.6–20.4 and 18.6–20%, respectively. Foliar nutrition showed the largest net returns from Zn0.5%. The water-use efficiency (WUE) and production efficiency increased by 18.4–37.6 and 34.9–37.5%, respectively, due to Zn0.5% over the efficiencies from NFN treatment. Monetary efficiency (ME) gains due to Zn0.5% were 24% higher, while ME efficiency gains due to urea2%, NPK2%, and B0.5% varied between 15–16%. Thus, this study suggested that the foliar application of 0.5% Zn and B, urea, NPK fertilizer, and DAP at 2%, along with RDF. is a profitable nutrient management option for quality soybean production in a semiarid region. However, nutrient partitioning, changes in soil chemical and biological indicators, and environmental aspects need critical examination in future studies.
Comparative Study on the Efficiency of Simulation and Meta-Model-Based Monte Carlo Techniques for Accurate Reliability Analysis of Corroded Pipelines
Authors: Seghier Mohamed El Amine Ben , Spyridis Panagiotis , Jafari-Asl Jafar , Ohadi Sima , Li Xinhong
Published: May 2022

Estimation of the failure probability for corroded oil and gas pipelines using the appropriate reliability analysis method is a task with high importance. The accurate prediction of failure probability can contribute to the better integrity management of corroded pipelines. In this paper, the reliability analysis of corroded pipelines is investigated using different simulation and meta-model methods. This includes five simulation approaches, i.e., Monte Carlo Simulation (MCS), Directional Simulation (DS), Line Sampling (LS), Subset Simulation (SS), and Importance Sampling (IS), and two meta-models based on MCS as Kriging-MCS and Artificial Neural Network based on MCS (ANN-MCS). To implement the proposed approaches, three limit state functions (LSFs) using probabilistic burst pressure models are established. These LSFs are designed for describing the collapse failure mode for pipelines constructed of low, mid, and high strength steels and are subjected to corrosion degradation. Illustrative examples that comprise three candidate pipelines made of X52, X65, and X100 steel grade are employed. The performance and efficiency of the proposed techniques for the estimation of the failure probability are compared from different aspects, which can be a useful implementation to indicate the complexity of handling the uncertainties provided by corroded pipelines.
Credit Risk Evaluation of Forest Farmers under Internet Crowdfunding Mode: The Case of China’s Collective Forest Regions
Authors: Wu Xiaowo , Tu Jiangwei , Liu Boru , Zhou Xi , Wu Yanxiong
Published: May 2022

To effectively quantify and control the credit risk of forest farmers under internet crowdfunding mode, the combined weighting of norm grey correlation, the improved analytic hierarchy process and empirical mode decomposition method are proposed to measure the credit risk, and the interval rough number DEMATEL method is used to analyze the credit risk factors of forest farmers. Through the calculation of comprehensive influence degree, it is concluded that the degree of investor information asymmetry, the intensity of supervision, the degree of innovation and cooperation between funders and investors are the main credit risk factors of forest farmers under internet crowdfunding mode, and a credit risk control mechanism is constructed according to the main credit risk factors to effectively improve the risk management and control level of forest farmers.
The Effect of Social Networks on Smallholder Farmers’ Decision to Join Farmer-Base Seed Producer Cooperatives (FBSc): The Case of Hararghe, Oromia, Ethiopia
Authors: Ofolsha Mulu Debela , Kenee Fekadu Beyene , Bimirew Dawit Alemu , Tefera Tesfaye Lemma , Wedajo Aseffa Seyoum
Published: May 2022

The paper presents the results of analysis conducted to empirically establish the role of social network in smallholder farmers’ decision to join seed producer cooperative, in Hararghe, Oromia, Ethiopia. We used a ‘random matching within sample’ technique to generate data on social links and resources shared like information among their links. Average treatment effects were used to limit ‘non-awareness’ or ‘selection bias’ on participation rate. An econometric strategy was employed to isolate endogenous effect from correlated and contextual social network effects and analyzed by Probit model. Rainfall data was obtained from NMA for 1986–2018 years and analyzed by coefficient of variation (CV) and standardized anomalies index (Z). The result CV and Z shows intra- and inter seasonal variability of rainfall and, the severity and frequency of drought have increase which works against a single optimum seed source. Rate of membership to FBSc was 75% for exposed sub-sample against 70% for the full sample. This shows the existence of exposure bias. The size of social network, linkage with relatives and extension agents influence farmers’ decision to join FBSc and thus, the presence of endogenous effects of social networks. Thus, participation decision is more likely to be affected by characters and structure of social network. Access to off-farm income and perception on profit influences farmer participation decision where weather-indexed seed insurance can service as an effective strategy to ensure sustained membership. Therefore, any intervention in promotion of FBSc should consider the characters’ and structure of social network and emphasis on indexed-based seed insurance and climate information services as strategy to promote seed producers cooperatives.
Cooperative Resilience during the Pandemic: Indonesia and Malaysia Evidence
Authors: Yuhertiana Indrawati , Zakaria Maheran , Suhartini Dwi , Sukiswo Helmy Wahyu
Published: May 2022

Two issues occur related to cooperatives’ entity resilience during the COVID-19 pandemic. The first comes from external stakeholders increasing the role of government in empowering cooperatives. Second is the strengthening of solidarity cooperatives members demonstrate in helping each other. To address these issues, the purpose of this study will be to explore the role of government in supporting cooperative resilience and to observe the internal values of cooperatives. Examining these two concerns will reveal how cooperatives engage external and internal stakeholders to handle unforeseen circumstances. Data were collected from two cognate countries, Indonesia and Malaysia. To gather data, 9 informants were interviewed, and 14 YouTube channels and 6 government websites were observed. The data were transcribed and analyzed. These data lead to the following two themes: the significant government support and the tight connected feeling of members to help each other, called gotong royong. These themes will be useful for understanding the optimal way to increase the engagement of external and internal cooperative stakeholders, as the key to cooperative resilience. It was found that government has a major role in the development of cooperatives, in both countries, Indonesia and Malaysia. Gotong royong, as the spirit of the cooperative movement, especially in Indonesia, saved the cooperative industry during the pandemic. This study supports the stakeholder theory and social capital theory and contributes to the literature on the practices of cooperatives in two countries.
Microclimatic and Environmental Improvement in a Mediterranean City through the Regeneration of an Area with Nature-Based Solutions: A Case Study
Authors: Perini Katia , Calise Chiara , Castellari Paola , Roccotiello Enrica
Published: May 2022

Dense urban areas are facing relevant issues related to their high vulnerability to the impacts of climate change and ecosystem health. The study presents a case study of a regeneration project with Nature-based Solutions in the city of Genoa (Italy) and, more specifically, in a neighbourhood characterised by relevant health and well-being issues. The performances of three design scenarios for a city hotspot, including plant species selected with a systemic approach and light pavements, are analysed in terms of improved microclimate by means of the ENVI-met software V4.4.5. The results show different benefits on the microclimate compared to the current state depending on the different scenarios: A UTCI decrease from 4.1 °C to 5.4 °C, a reduction of mean radiant temperature from 12.3 °C to 17.3 °C, a relative humidity increase from 3.8% to 5.6%, and a progressive decrease in wind speed are detected in a directly proportional way to the gradual increase in greenery inside the scenarios. In reverse, better results for air temperatures are detected for the scenario with less greening (Δt = 1.8 °C). The study relies on the re-parametrisation of plant species characteristics in the ENVI-met database to reach a high level of accuracy.