bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-Time Step Length Estimation in Indoor and Outdoor Scenarios
Authors: Yang Zanru , Tran Le Chung , Safaei Farzad , Le Anh Tuyen , Taparugssanagorn Attaphongse
Published: November 2022

In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is “sliding” forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.
Video Super-Resolution Method Using Deformable Convolution-Based Alignment Network
Authors: Lee Yooho , Cho Sukhee , Jun Dongsan
Published: November 2022

With the advancement of sensors, image and video processing have developed for use in the visual sensing area. Among them, video super-resolution (VSR) aims to reconstruct high-resolution sequences from low-resolution sequences. To use consecutive contexts within a low-resolution sequence, VSR learns the spatial and temporal characteristics of multiple frames of the low-resolution sequence. As one of the convolutional neural network-based VSR methods, we propose a deformable convolution-based alignment network (DCAN) to generate scaled high-resolution sequences with quadruple the size of the low-resolution sequences. The proposed method consists of a feature extraction block, two different alignment blocks that use deformable convolution, and an up-sampling block. Experimental results show that the proposed DCAN achieved better performances in both the peak signal-to-noise ratio and structural similarity index measure than the compared methods. The proposed DCAN significantly reduces the network complexities, such as the number of network parameters, the total memory, and the inference speed, compared with the latest method.
A Lightweight Vehicle-Pedestrian Detection Algorithm Based on Attention Mechanism in Traffic Scenarios
Authors: Zhang Yong , Zhou Aibo , Zhao Fengkui , Wu Haixiao
Published: November 2022

Object detection is a critical technology of environmental perception for autonomous driving vehicle. The Convolutional Neural Network has gradually become a powerful tool in the field of vehicle detection because of its powerful ability of feature extraction. In aiming to reach the balance between speed and accuracy of detection in complex traffic scenarios, this paper proposes an improved lightweight and high-performance vehicle–pedestrian detection algorithm based on the YOLOv4. Firstly, the backbone network CSPDarknet53 is replaced by MobileNetv2 to reduce the number of parameters and raise the capability of feature extraction. Secondly, the method of multi-scale feature fusion is used to realize the information interaction among different feature layers. Finally, a coordinate attention mechanism is added to focus on the region of interest in the image by way of weight adjustment. The experimental results show that this improved model has a great performance in vehicle–pedestrian detection in traffic scenarios. Experimental results on PASCAL VOC datasets show that the improved model’s mAP is 85.79% and speed is 35FPS, which has an increase of 4.31% and 16.7% compared to YOLOv4. Furthermore, the improved YOLOv4 model maintains a great balance between detection accuracy and speed on different datasets, indicating that it can be applied to vehicle–pedestrian detection in traffic scenarios.
Electromagnetic Fields Exposure Assessment in Europe Utilizing Publicly Available Data
Authors: Iakovidis Serafeim , Apostolidis Christos , Manassas Athanasios , Samaras Theodoros
Published: November 2022

The ever-increasing use of wireless communication systems during the last few decades has raised concerns about the potential health effects of electromagnetic fields (EMFs) on humans. Safety limits and exposure assessment methods were developed and are regularly updated to mitigate health risks. Continuous radiofrequency EMF monitoring networks and in situ measurement campaigns provide useful information about environmental EMF levels and their variations over time and in different microenvironments. In this study, published data from the five largest monitoring networks and from two extensive in situ measurement campaigns in different European countries were gathered and processed. Median electric field values for monitoring networks across different countries lay in the interval of 0.67–1.51 V/m. The median electric field value across different microenvironments, as evaluated from in situ measurements, varied from 0.10 V/m to 1.42 V/m. The differences between networks were identified and mainly attributed to variations in population density. No significant trends in the temporal evolution of EMF levels were observed. The influences of parameters such as population density, type of microenvironment, and height of measurement on EMF levels were investigated.
An “Instantaneous” Response of a Human Visual System to Hue: An EEG-Based Study
Authors: Tcheslavski Gleb V. , Vasefi Maryam
Published: November 2022

(1) The article presents a new technique to interpret biomedical data (EEG) to assess cortical responses to continuous color/hue variations. We propose an alternative approach to analyze EEG activity evoked by visual stimulation. This approach may augment the traditional VEP analysis. (2) Considering ensembles of EEG epochs as multidimensional spatial vectors evolving over time (rather than collections of time-domain signals) and evaluating the similarity between such vectors across different EEG epochs may result in a more accurate detection of colors that evoke greater responses of the visual system. To demonstrate its suitability, the developed analysis technique was applied to the EEG data that we previously collected from 19 participants with normal color vision, while exposing them to stimuli of continuously varying hue. (3) Orange/yellow and dark blue/violet colors generally aroused better-pronounced cortical responses. The selection of EEG channels allowed for assessing the activity that predominantly originates from specific cortical regions. With such channel selection, the strongest response to the hue was observed from Parieto-Temporal region of the right hemisphere. The statistical test—Kruskal–Wallis one-way analysis of variance—indicates that the distance evaluated for spatial EEG vectors at different post-stimulus latencies generally originate from different statistical distributions with a probability exceeding 99.9% (α = 0.001).
Design and Performance of Extraordinary Low-Cost Compact Terahertz Imaging System Based on Electronic Components and Paraffin Wax Optics
Authors: Tamošiūnas Vincas , Minkevičius Linas , Bučius Ignotas , Jokubauskis Domas , Redeckas Karolis , Valušis Gintaras
Published: November 2022

Terahertz (THz) imaging is a powerful technique allowing us to explore non-conducting materials or their arrangements such as envelopes, packaging substances, and clothing materials in a nondestructive way. The direct implementation of THz imaging systems relies, on the one hand, on their convenience of use and compactness, minimized optical alignment, and low power consumption; on the other hand, an important issue remains the system cost and its figure of merit with respect to the image quality and recording parameters. In this paper, we report on the design and performance of an extraordinary low-cost THz imaging system relying on a InP Gunn diode emitter, paraffin wax optics, and commercially available GaAs high-electron-mobility transistors (HEMTs) with a gate length of 200 nm as the sensing elements in a room temperature environment. The design and imaging performance of the system at 94 GHz is presented, and the spatial resolution in the range of the illumination wavelength (∼3 mm) and contrast of nearly two orders of magnitude is determined. The operation of two models of the HEMTs of the same nominal 20 GHz cut-off frequency, but placed in different packages and printed circuit board layouts was evaluated at 94 GHz and 0.307 THz. The presence of two competing contributions—self-resistive mixing and radiation coupling through the antenna effects of the printed circuit boards—to the detected signal is revealed by the signal dependence on the gate-to-source voltage, resulting in a cross-sectional responsivity of 27 V/W and noise-equivalent power of 510 pW/Hz at 94 GHz. Further routes in the development of low-cost THz imaging systems in the range of EUR 100 are considered.
Using Sensors for Player Development: Assessing Biomechanical Factors Related to Pitch Command and Velocity
Authors: Agresta Cristine , Freehill Michael T. , Nakamura Bryson , Guadagnino Samuel , Cain Stephen M.
Published: November 2022

Pitching biomechanical research is highly focused on injury prevention with little attention to how biomechanical data can facilitate skill development. The overall purpose of this study was to explore how sensor-derived segment kinematics and timing relate to command and ball velocity during baseball pitching. We used a cross-sectional design to analyze a series of pitches thrown from 10 collegiate baseball pitchers. We collected biomechanical data from six inertial sensors, subjective command from the pitchers, and ball velocity from a radar device. Stepwise regression analyses were used to explore biomechanical variables associated with command for all pitches and ball velocity for fastballs only. We found that only peak forearm linear acceleration was significantly associated with command, whereas several segment kinematic measures were significantly associated with ball velocity. Our results suggest that different biomechanical variables are linked to specific pithing skills. Our findings suggest that end-effector (forearm) movement is more important for pitch command, whereas proximal-to-distal (pelvis, trunk, upper arm, forearm) segmental movement is important for ball velocity.
Cost-Effective Corrosion Detection Sensor for Above-Ground Oil and Gas Flowlines
Authors: Vahdati Nader , Shiryayev Oleg , Parapurath Shahid M. , Yap Fook F. , Butt Haider
Published: November 2022

A sensor for monitoring of the external corrosion of small-diameter aboveground oil and gas pipelines (called flowlines), based on fiber-optic strain sensing, is proposed. The working principle of our proposed sensor relies on the use of a pre-stressed sacrificial structure made of the same material as the pipeline and monitoring changes in the measured strain that occur due to deterioration caused by corrosion to the structure. We present the development of analytical equations that allow designing the sensor structure to achieve the desired strain values. The analysis was verified using commercial finite element analysis (FEA) software. The proposed sensor is simple and cost-effective and can be easily manufactured. It can be deployed on existing overground pipelines without any modification to the pipeline structure. While it is not capable of measuring the corrosion rate continuously, it can provide a measurement of the average corrosion rate over the life span of its sacrificial metal structure.
A Comparative Study on the Temporal Effects of 2D and VR Emotional Arousal
Authors: Tian Feng , Wang Xuefei , Cheng Wanqiu , Lee Mingxuan , Jin Yuanyuan
Published: November 2022

Previous research comparing traditional two-dimensional (2D) and virtual reality with stereoscopic vision (VR-3D) stimulations revealed that VR-3D resulted in higher levels of immersion. However, the effects of these two visual modes on emotional stimulus processing have not been thoroughly investigated, and the underlying neural processing mechanisms remain unclear. Thus, this paper introduced a cognitive psychological experiment that was conducted to investigate how these two visual modes influence emotional processing. To reduce fatigue, participants (n = 16) were randomly assigned to watch a series of 2D and VR-3D short emotional videos for two days. During their participation, electroencephalograms (EEG) were recorded simultaneously. The results showed that even in the absence of sound, visual stimuli in the VR environment significantly increased emotional arousal, especially in the frontal region, parietal region, temporal region, and occipital region. On this basis, visual evoked potential (VEP) analysis was performed. VR stimulation compared to 2D led to a larger P1 component amplitude, while VEP analysis based on the time course of the late event-related potential component revealed that, after 1200 ms, the differences across visual modes became stable and significant. Furthermore, the results also confirmed that VEP in the early stages is more sensitive to emotions and presumably there are corresponding emotion regulation mechanisms in the late stages.
Evaluation of an AI-Based TB AFB Smear Screening System for Laboratory Diagnosis on Routine Practice
Authors: Fu Hsiao-Ting , Tu Hui-Zin , Lee Herng-Sheng , Lin Yusen Eason , Lin Che-Wei
Published: November 2022

The most robust and economical method for laboratory diagnosis of tuberculosis (TB) is to identify mycobacteria acid-fast bacilli (AFB) under acid-fast staining, despite its disadvantages of low sensitivity and labor intensity. In recent years, artificial intelligence (AI) has been used in TB-smear microscopy to assist medical technologists with routine AFB smear microscopy. In this study, we evaluated the performance of a TB automated system consisting of a microscopic scanner and recognition program powered by artificial intelligence and machine learning. This AI-based system can detect AFB and classify the level from 0 to 4+. A total of 5930 smears were evaluated on the performance of this automatic system in identifying AFB in daily lab practice. At the first stage, 120 images were analyzed per smear, and the accuracy, sensitivity, and specificity were 91.3%, 60.0%, and 95.7%, respectively. In the second stage, 200 images were analyzed per smear, and the accuracy, sensitivity, and specificity were increased to 93.7%, 77.4%, and 96.6%. After removing disqualifying smears caused by poor staining quality and smear preparation, the accuracy, sensitivity, and specificity were improved to 95.2%, 85.7%, and 96.9%, respectively. Furthermore, the automated system recovered 85 positive smears initially identified as negative by manual screening. Our results suggested that the automated TB system could achieve higher sensitivity and laboratory efficiency than manual microscopy under the quality control of smear preparation. Automated TB smear screening systems can serve as a screening tool at the first screen before manual microcopy.
DFTrans: Dual Frequency Temporal Attention Mechanism-Based Transportation Mode Detection
Authors: Wang Pu , Jiang Yongguo
Published: November 2022

In recent years, with the diversification of people’s modes of transportation, a large amount of traffic data is generated when people travel every day, and this data can help transportation mode detection to be of great use in a variety of applications. Although transportation mode detection has been investigated, there are still challenges in terms of accuracy and robustness. This paper presents a novel transportation mode detection algorithm, DFTrans, which is based on Temporal Block and Attention Block. Low- and high-frequency components of traffic sequences are obtained using discrete wavelet transforms. A two-channel encoder is carefully designed to accurately capture the temporal and spatial correlation between low- and high-frequency components in both long- and short-term patterns. With the Temporal Block, the inductive bias of the CNN is introduced at high frequencies to improve generalization performance. At the same time, the network is generated with the same length as the input, ensuring a long effective history. Low frequencies are passed through Attention Block, which has fewer parameters to capture the global focus and solves the problem that RNNs cannot be computed in parallel. After fusing the output of the feature by Temporal Block and Attention Block, the classification results are output by MLP. Extensive experimental results show that the DFTrans algorithm achieves macro F1 scores of 86.34% on the real-world SHL dataset and 87.64% on the HTC dataset. Our model can better identify eight modes of transportation, including stationary, walking, running, cycling, bus, car, underground, and train, and has better performance in transportation mode detection than other baseline algorithms.
Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System
Authors: Fraga-Lamas Paula , Barros Daniel , Lopes Sérgio Ivan , Fernández-Caramés Tiago M.
Published: November 2022

While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution (e.g., a good trade-off between cost, accuracy, resilience and computational efficiency) that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.
Two Compensation Strategies for Optimal Estimation in Sensor Networks with Random Matrices, Time-Correlated Noises, Deception Attacks and Packet Losses
Authors: Caballero-Águila Raquel , Hu Jun , Linares-Pérez Josefa
Published: November 2022

Due to its great importance in several applied and theoretical fields, the signal estimation problem in multisensor systems has grown into a significant research area. Networked systems are known to suffer random flaws, which, if not appropriately addressed, can deteriorate the performance of the estimators substantially. Thus, the development of estimation algorithms accounting for these random phenomena has received a lot of research attention. In this paper, the centralized fusion linear estimation problem is discussed under the assumption that the sensor measurements are affected by random parameter matrices, perturbed by time-correlated additive noises, exposed to random deception attacks and subject to random packet dropouts during transmission. A covariance-based methodology and two compensation strategies based on measurement prediction are used to design recursive filtering and fixed-point smoothing algorithms. The measurement differencing method—typically used to deal with the measurement noise time-correlation—is unsuccessful for these kinds of systems with packet losses because some sensor measurements are randomly lost and, consequently, cannot be processed. Therefore, we adopt an alternative approach based on the direct estimation of the measurement noises and the innovation technique. The two proposed compensation scenarios are contrasted through a simulation example, in which the effect of the different uncertainties on the estimation accuracy is also evaluated.
Vibration Energy Conversion Power Supply Based on the Piezoelectric Thin Film Planar Array
Authors: Wang Bo , Lan Dun , Zeng Fanyang , Li Wei
Published: November 2022

Vibration energy harvesting has received much attention as a new type of power solution for low-power micro/nano-devices. However, VEH (vibration energy harvester) based on PVDF (polyvinylidene fluoride) piezoelectric materials have a low output power and energy conversation efficiency due to the relatively low piezoelectric constant, coupling coefficient, and dielectric constant. For this reason, we design a vibration energy conversion power supply, which consists of a VEH with a PVDF piezoelectric thin film planar array vibration structure and an energy harvesting circuit for regulating the electric energy of multiple sources. Furthermore, our solution was validated by simulations of structural dynamics in COMSOL and equivalent circuits model in Multisim. From the circuitry simulation results, the output current and the charging period increase and decrease, doubling, respectively, for each doubling of the number of array groups of films. Moreover, the solid mechanics simulation results show that the planar array structure makes the phase and amplitude of the input vibration waves as consistent as possible so that the same theoretical enhancement effect of the circuitry model is achieved. An identical experimental test was implemented with vibration conditions of 75 Hz-2.198 g. The fabricated harvester quickly charged the 22 V-0.022 F ultracapacitor bank to 5 V in 24 min. The maximum open circuit voltage and output power, respectively, were 10.4 V and 0.304 mW. This maximum charging power was 11.69 times higher than that of a single film. This special power supply can replace batteries to power low-power electronics deployed in vibrating environments, thus reducing the maintenance costs of equipment and environmental pollution rates.
Anti-Sporotrichotic Activity, Lambert-W Inhibition Kinetics and 3D Structural Characterization of Sporothrix schenckii Catalase as Target of Glucosinolates from Moringa oleifera
Authors: Sierra-Campos Erick , Valdez-Solana Mónica A. , Ruiz-Baca Estela , Ventura-García Erica K. , Avitia-Domínguez Claudia I. , Aguilera-Ortiz Miguel , Téllez-Valencia Alfredo
Published: November 2022

Most human fungal infections exhibit significant defensive oxidative stress responses, which contribute to their pathogenicity. An important component of these reactions is the activation of catalase for detoxification. To discover new antifungal chemicals, the antifungal activity of methanol extracts of Moringa oleifera from two commercial products (Akuanandi and Mas Lait) was investigated. The methanolic extracts’ activity against Sporothrix schenckii was determined using an assay for minimum inhibitory concentration (MIC) and minimum lethal concentration (MLC). The MIC concentrations varied between 0.5 μg/mL and 8 μg/mL. Akuanandi extract had the lowest MIC (0.5 μg/mL) and MLC (1 μg/mL) values. M. oleifera methanolic extracts were tested for catalase inhibition. The Ki values of the M. oleifera extract against S. schenckii catalase (SsCAT) was found to be 0.7 μg/mL for MOE-AK and 0.08 μg/mL for MOE-ML. Catalase’s 3D structure in SsCAT is unknown. The homology of SsCAT was modeled with an in silico study using a 3D structure from SWISS MODEL and validation the predicted 3D structure was carried out using PROCHECK and MolProbity. Docking simulations were used to analyze protein interactions using Pymol, PoseView, and PLIP. The results revealed that M. oleifera glucosinolates interacts with SsCAT. A molecular interaction analysis revealed two inhibitor compounds (glucosinalbin and glucomoringin) with high binding affinity to key allosteric-site residues. The binding energies revealed that glucosinalbin and glucomoringin bind with high affinity to SsCAT (docking energy values: −9.8 and −9.0 kcal/mol, respectively). The findings of this study suggest that glucosinolates derived from M. oleifera could be used instead of synthetic fungicides to control S. schenckii infections. We hope that the findings of this work will be valuable for developing and testing novel natural anti-sporothrix therapeutic agents in the future.
Advances in Accurate Quantification Methods in Food Analysis
Authors: Li Xianjiang , Weng Rui
Published: November 2022

Food safety is an important topic, and with the perfection of regulations and technologies, food safety is improving [...]
Application of Nanofiltration Membrane Based on Metal-Organic Frameworks (MOFs) in the Separation of Magnesium and Lithium from Salt Lakes
Authors: Liu Yueyu , Zhu Rong , Srinivasakannan Chandrasekar , Li Tingting , Li Shiwei , Yin Shaohua , Zhang Libo
Published: November 2022

With the increasing demand for lithium, the shortage of resources has become increasingly apparent. In order to conserve resources and to improve recovery, the extraction of lithium from salt lakes has become mandatory for sustainable development. Porous metal-organic framework (MOF) materials have attracted extensive attention due to their high/tunable porosity, pore function, multiple pore structures/compositions, and open metal sites. Moreover, MOFs combine the advantages of other porous materials and have a wide range of applications, which have received significant interest from the scientific community. Therefore, the selection of MOFs materials, the optimization of preparation methods, and the research of lithium separators are key directions to improve the total yield of lithium resources in salt lakes in China. This study aims to improve the comprehensive utilization of resources after lithium extraction and strengthen the engineering technology research of lithium extraction from salt lakes. This study can help to achieve the goal of efficient, integrated, and sustainable utilization of salt lake resources. An attempt has been made to summarize the types and preparation methods of MOFs materials, as well as the separation mechanism of MOFs nanofiltration membranes, with reference to its application in lithium extraction from salt lake brine. Finally, the future development of MOFs nanofiltration membranes for lithium extraction from salt lakes is also proposed.
A Straightforward Approach to Drawing Temperature-Dependent I–V Curves of Solar Cell Models
Authors: Klein Rolf
Published: November 2022

Equivalent circuit models of solar cells are important for understanding the behavior of photovoltaic systems under different weather conditions. They provide an equation F(V,I)=0 that expresses the correspondence between voltage V and current I a cell can deliver. The performance of a cell, and, therefore, the parameters of equation F, depend on the cell’s temperature and on the incoming light’s energy and angle. One would like to simulate and visualize these dependencies in real time. Given a fixed set of parameters, no elementary solution s(V)=I of Equation F(V,I)=0 is known. Hence, circuit simulation systems employ numerical methods to solve this equation and to approximate the circuit’s I–V curve, CIV. In this note, we propose a simpler approach. Instead of expressing I as a function of V, we represent both as elementary functions V(u) and I(u) of a real parameter u. In this way, the I–V curve CIV is obtained as the image of the mapping m(u)=(V(u),I(u)) from a u-interval to the VI-plane. Our approach offers both a precise mathematical description of CIV and an easy way to draw it. This allows us to study the influence of environmental changes on CIV by smooth animations, and yet with rather simple means. In this paper, we consider temperature dependence as an example; changes in irradiance or angle could be incorporated as well. Using formulae suggested in the literature that describe how the parameters in equation F(V,I)=0 depend on temperature, it takes only a few lines of code to generate an interactive worksheet that shows how CIV, the location of the maximum power point MPP and the maximum power change as the circuit’s temperature, is altered on a slider. Such a worksheet and its location will be presented in this paper.
Prediction of Thermal Energy Demand Using Fuzzy-Based Models Synthesized with Metaheuristic Algorithms
Authors: Alkhazaleh Hamzah Ali , Nahi Navid , Hashemian Mohammad Hossein , Nazem Zohreh , Shamsi Wameed Deyah , Nehdi Moncef L.
Published: November 2022

Increasing consumption of energy calls for proper approximation of demand towards a sustainable and cost-effective development. In this work, novel hybrid methodologies aim to predict the annual thermal energy demand (ATED) by analyzing the characteristics of the building, such as transmission coefficients of the elements, glazing, and air-change conditions. For this objective, an adaptive neuro-fuzzy-inference system (ANFIS) was optimized with equilibrium optimization (EO) and Harris hawks optimization (HHO) to provide a globally optimum training. Moreover, these algorithms were compared to two benchmark techniques, namely grey wolf optimizer (GWO) and slap swarm algorithm (SSA). The performance of the designed hybrids was evaluated using different accuracy indicators, and based on the results, ANFIS-EO and ANFIS-HHO (with respective RMSEs equal to 6.43 and 6.90 kWh·m−2·year−1 versus 9.01 kWh·m−2·year−1 for ANFIS-GWO and 11.80 kWh·m−2·year−1 for ANFIS-SSA) presented the most accurate analysis of the ATED. Hence, these models are recommended for practical usages, i.e., the early estimations of ATED, leading to a more efficient design of buildings.
Social Media Marketing for Small and Medium Enterprise Performance in Uganda: A Structural Equation Model
Authors: Kikawa Cliff R. , Kiconco Charity , Agaba Moses , Ntirampeba Dimas , Ssematimba Amos , Kalema Billy M.
Published: November 2022

Thanks to the ongoing expansion of internet access and, most recently, the movement restrictions that were put in place globally to stop COVID-19 spread, many small and medium enterprises (SMEs) are prepared to use social media platforms to market their products as a way to improve their business performance in emerging economies. Businesses at all levels that use social media marketing (SMM) see a considerable increase in their output. This study’s objective is to identify the factors that significantly help Ugandan SMEs implement SMM techniques to enhance their commercial performance. Here, statistical models are utilized to analyze how the age and gender of SMEs owners as moderating variables affect the adoption and performance of SMEs in Uganda. A theoretical model that is based on the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) theories has been put out as part of a specific conceptual framework. The following indicators are used to evaluate the performance of SMEs: sales, customer engagement, awareness of customers’ needs, low operation costs, and brand modification by staff. Empirical model validation has been performed using 152 business units (observation units) corresponding to the number of respondents (units of analysis), and the ensuing analyses have been done using structural equation modelling (SEM). The results indicate that compatibility and perceived ease of use have a positive impact on SMEs to adopt SMM, while perceived usefulness has a negative impact on SMEs to adopt SMM. Age and gender as moderating variables all have a positive moderating effect. With limited studies available on the subject, this research contributes to already existing literature by combining two components of the TAM model and one component of the IDT to explain the impact of SMM on SMEs when moderated by both age and gender in a developing economy.
Connecting Digital Channels to Consumers’ Purchase Decision-Making Process in Online Stores
Authors: Pires Paulo Botelho , Santos José Duarte , Brito Pedro Quelhas de , Marques David Nunes
Published: November 2022

This research establishes the relationship between the digital channels that organizations use to communicate with their audience and the stages of the consumer buying decision process in online stores. Researchers have not treated this relationship in much detail and little-known empirical research has focused on exploring relationships between the two subjects. Establishing this relationship is of crucial importance for organizations and consumers, as it ensures organizations use the digital channels that consumers want. A literature review of digital channels and consumer behavior models was performed, which allowed us to define which are the digital channels and to identify the different models of consumer behavior appropriate for the digital age. A quantitative methodology was used, supported on a questionnaire that allowed us to find out which digital channels are the most appropriate for each stage of the buying decision process. The results show that consumers use more than one digital channel at each stage of the buying decision process and for each stage, a set of digital channels is identifiable that is most preferred. In light of the above, those who are responsible for defining the digital marketing strategy know what types of content they should produce for each digital channel, allowing them to guarantee efficiency in the use of resources while ensuring that consumers get what they want.
Current Challenges in the Adoption of Digital Visual Management at Construction Sites: Exploratory Case Studies
Authors: Reinbold Ana , Lappalainen Eelon , Seppänen Olli , Peltokorpi Antti , Singh Vishal
Published: November 2022

In the construction industry, digitalisation has led to increasing efforts to improve construction management using digital visual management (VM) devices. Although the amount of research on digital VM (DVM) in the design phase and in the management of construction sites has also increased, its implementation during the production phase and by construction crews remains limited. The objective of this study is to explore the adoption of DVM in construction sites, assess construction workers’ experiences regarding digital and analogue VM devices, and understand the challenges that hinder the adoption of such devices. This study used a mixed method approach, combining qualitative and quantitative research. Data included visual site explorations, surveys of construction workers and crew managers, and unstructured interviews with site managers and development directors to assess the use of DVM devices in construction sites, the need for them and their current implementation. The findings showed that VM should be conveniently located near the job site instead of the office site, which is the current situation. Construction crews who experienced more production and schedule disruptions reported that VM supported their work, compared with crews that had fewer disruptions. VM devices on construction sites are analogue, and their usage continues to be in construction site management, which perpetuates information silos during construction projects. The findings of this study provide insights into the development and deployment of DVM devices on construction sites. Construction workers’ need for visual information close at hand is of interest to both scholars and practitioners in future research and development.
Customized Instance Random Undersampling to Increase Knowledge Management for Multiclass Imbalanced Data Classification
Authors: Tusell-Rey Claudia C. , Camacho-Nieto Oscar , Yáñez-Márquez Cornelio , Villuendas-Rey Yenny
Published: November 2022

Imbalanced data constitutes a challenge for knowledge management. This problem is even more complex in the presence of hybrid (numeric and categorical data) having missing values and multiple decision classes. Unfortunately, health-related information is often multiclass, hybrid, and imbalanced. This paper introduces a novel undersampling procedure that deals with multiclass hybrid data. We explore its impact on the performance of the recently proposed customized naïve associative classifier (CNAC). The experiments made, and the statistical analysis, show that the proposed method surpasses existing classifiers, with the advantage of being able to deal with multiclass, hybrid, and incomplete data with a low computational cost. In addition, our experiments showed that the CNAC benefits from data sampling; therefore, we recommend using the proposed undersampling procedure to balance data for CNAC.
Experience of Pro-Poor Tourism (PPT) in China: A Sustainable Livelihood Perspective
Authors: Wang Zhaoguo , Dong Fengli
Published: November 2022

Pro-poor tourism is a powerful tool in China’s poverty alleviation strategy, helping the achievement of Sustainable Development Goal 1, no poverty. Thus, the Chinese experience in tourism poverty reduction could be instructive. Considering the dominant role of the government in PPT, this study examines the government’s PPT scheme within a sustainable livelihood framework, uncovering the usefulness of PPT in poverty alleviation. With thematic analysis, 18 cases are systematically coded, and several findings are discovered. Rural development is co-evolutionary with PPT, while livelihood capitals change correspondingly. Specifically, human capital is mentioned as the top priority, followed by economic capital, institutional capital, social capital, and natural capital. Analysis of the cases indicates that livelihood capital comprises multiple themes, and a variety of livelihood strategies are applied conditionally. Additionally, livelihood outcomes are in accord with the criteria of Beautiful Village, characterized by good quality of life. In summary, the success of the PPT in China is a comprehensive project, contributed to by a government-led model, a well-organized community system, effective community participation, and whole-of-society synergy. The study demonstrates that a paradigm shift has been seen in China’s PPT model and sheds light on tourism development in poverty alleviation globally.
European Green Transition Implications on Africa’s Livestock Sector Development and Resilience to Climate Change
Authors: Ndue Kennedy , Pál Goda
Published: November 2022

Green growth and the transition towards green growth are gaining scientific and public interest across Africa at an unprecedented rate. The Paris Agreement ratification by all 54 member states and the African Union (AU) goals in its Agenda 2063 on green economies are sufficient evidence of this. This is in line with the European Green Deal (EGD) aspirations, which envisages making Europe a carbon neutral economy by 2050. One of the EGD’s four main pillars is sustainable food systems. The success of EGD is premised on its ability to inspire and support green transition and effective climate action globally. The borderless nature of climate change necessitates a holistic approach to ensure the EU’s green transition does not come at the cost of development elsewhere. The main challenge is finding Africa’s space and position within the desired holistic approach, as Africa’s economy is agriculturally driven. One key African agricultural sub-sector significantly impacting livelihoods is livestock, which supports up to 80% of the rural livelihoods and which grapples with challenges in satisfying the needs of a fast-growing population. What could the EU green transition mean to this sector? We established that between 2010–2019, the African livestock population grew exponentially, and feed production followed the same path, with the share of land under forests, grasslands and meadows declining drastically. Over the same period, the percentage of land under arable farming increased while the animal-based protein and meat imports curve grew exponentially. This situation puts the continent in a dilemma about finding a sustainable solution for the food–feed and environmental nexus. Against this backdrop, a myriad of questions arises on how the green transition can be established to promote mitigating any loss that might occur in the process. We conducted a detailed sectoral trend analysis based on Food and Agricultural Organization (FAO) statistics to find plausible solutions and pathways to achieve a greener transition. We coupled it with intensive policy mapping to develop science-policy-driven solutions that could promote the green transition sustainably. To sustainably accelerate the sectoral growth trajectory while addressing climate change, we recommend adopting and implementing raft measures geared towards increased sectoral efficiency, effectiveness, innovativeness and a holistic approach to the problem. Adopting transformative policies can promote the sector’s competitiveness through incentivisation, technological adoption, financial support, market support and increased awareness of its importance in sustainable development. However, exercising caution in implementing these practices is crucial to ensure there is no leakage effect in implementing the EGD across Africa and beyond.
Relationship between Self-Concept, Emotional Intelligence and Problem-Solving Skills on Secondary School Students’ Attitude towards Solving Algebraic Problems
Authors: Abdullah Abdul Halim , Julius Elizabeth , Suhairom Nornazira , Ali Marlina , Abdul Talib Corrienna , Mohamad Ashari Zakiah , Abdul Kohar Umar Haiyat , Abd Rahman Sharifah Nurarfah S.
Published: November 2022

Mathematics is required from primary (basic) through junior secondary and senior secondary levels of education in Nigeria. The position of mathematics within the curriculum is reflective of the significance of the subject to the expansion of scientific knowledge and technological capability. However, student performance in mathematics, particularly algebraic aspects, is a challenge for Nigerian secondary school students. The purpose of this study was to investigate the relationship between self-concept (SC), emotional intelligence (EI), and problem-solving skills (PSS) on students’ attitudes towards solving algebraic problems (ATSAP). The study aimed at developing a model based on students’ SC, EI, PSS, and ATSAP. A total of 377 students were proportionately and randomly selected to collect the quantitative data. Two instruments: (i) a questionnaire measuring SC, EI, and ATSAP, and (ii) a test measuring PSS developed by the researcher, were used in this study. The data was analyzed by using structural equation modelling (SEM) and partial least square (SEM-PLS3). The major findings of the study revealed that secondary school students’ PSS could be improved with due consideration of their SC and EI. However, the three hypotheses tested indicated that there was a significant but negative relationship between SC and the students’ ATSAP (t > ±1.96, α = 0.05) and also there was a significant positive relationship between PSS and their ATSAP; however, the relationship between EI and their ATSAP was not significant (t < ±1.96, α = 0.05). Therefore, a model was developed based on the study’s findings. This model has a practical implication for the federal and state governments, curriculum planners, students, teachers, and parents.
Household Waste Control Index towards Sustainable Waste Management: A Study in Bekasi City, Indonesia
Authors: Ferdinan  , Utomo Suyud Warno , Soesilo Tri Edhi Budhi , Herdiansyah Herdis
Published: November 2022

Household waste is the most significant contributor to the accumulation of waste in the final processing area. Indonesia produces 65.2 million waste per year. Improper waste management practices in Indonesia require evaluation steps to achieve sustainable waste management. This study aims to formulate an evaluation instrument for the implementation of household waste management using the analysis of the Household Waste Control Index (HWCI) calculation. This study uses quantitative methods by collecting data from a survey of 548 waste bank members spread across 12 districts in Bekasi City and using the analysis of the calculation of the Household Waste Control Index. Based on the study results, it is known that the implementation of household waste management in Bekasi City is in the moderate category (5.86 out of 10), and the governance of household waste management at the community level still needs to be improved. Furthermore, based on the validation results, it is concluded that the household waste control index can be recommended as an instrument to measure, evaluate, and consider recommendations for waste management policies to realize sustainable household waste management in Bekasi City and other areas that have similar conditions to Bekasi City.
Subjective Norms or Psychological Empowerment? Moderation Effect of Power Distance on Knowledge Sharing
Authors: Dong Lingfeng , Ji Ting , Zhou Gan , Zhang Jie
Published: November 2022

This study examines the impacts of psychological empowerment and subjective norm on knowledge sharing in organizations, and the moderation effects aroused by power distance. Quantitative data from 567 valid questionnaires are collected by survey from a large company. The results demonstrate that subjective norm and psychological empowerment are positively associated with attitude toward knowledge sharing. Moreover, the findings further suggest that power distance undermines the influence of psychological empowerment on knowledge sharing but strengthens the effect of motivation to comply. Both theoretical and practical implications are discussed.
Ownership Structure and Carbon Emissions of SMEs: Evidence from OECD Countries
Authors: Ghachem Dorsaf Azouz , Basty Nadia , Zureigat Qasim
Published: November 2022

This work investigates the impact that the ownership structure of small- and medium-sized enterprises (SMEs) in Organization for Economic Co-operation and Development (OECD) countries exerts on the level of corporate carbon emissions, as well as the moderating effect of innovation on this relationship. Based on panel data from 32 OECD countries during 2015–2020, a pooled least-square panel model was developed for estimation. The results show that public, foreign, and institutional investors have a significant negative effect on carbon emissions. Conversely, strategic investors contribute to increasing carbon emissions. Moreover, findings provide evidence of mixed moderating effects of innovation on the relationship between types of owners and carbon emissions. Hence, strategic shareholders contribute to implementing environmental policies through innovation, while public and foreign investors incur Research and Development expenditures to boost firms’ economic activity, ignoring social and environmental commitments. Our results confirm the relationship between ownership structure and carbon emissions and the moderating effects of innovation on this association. Environmental innovation allows for improving worldwide firms’ competitiveness and long-term performance.