bluuebunny/crossref_metadata_2025 huggingface dataset:
Piezoelectric Materials and Sensors for Structural Health Monitoring: Fundamental Aspects, Current Status, and Future Perspectives
Authors: Ju Min , Dou Zhongshang , Li Jia-Wang , Qiu Xuting , Shen Binglin , Zhang Dawei , Yao Fang-Zhou , Gong Wen , Wang Ke
Published: January 2023

Structural health monitoring technology can assess the status and integrity of structures in real time by advanced sensors, evaluate the remaining life of structure, and make the maintenance decisions on the structures. Piezoelectric materials, which can yield electrical output in response to mechanical strain/stress, are at the heart of structural health monitoring. Here, we present an overview of the recent progress in piezoelectric materials and sensors for structural health monitoring. The article commences with a brief introduction of the fundamental physical science of piezoelectric effect. Emphases are placed on the piezoelectric materials engineered by various strategies and the applications of piezoelectric sensors for structural health monitoring. Finally, challenges along with opportunities for future research and development of high-performance piezoelectric materials and sensors for structural health monitoring are highlighted.
Plant-Wear: A Multi-Sensor Plant Wearable Platform for Growth and Microclimate Monitoring
Authors: Di Tocco Joshua , Lo Presti Daniela , Massaroni Carlo , Cinti Stefano , Cimini Sara , De Gara Laura , Schena Emiliano
Published: January 2023

Wearable devices are widely spreading in various scenarios for monitoring different parameters related to human and recently plant health. In the context of precision agriculture, wearables have proven to be a valuable alternative to traditional measurement methods for quantitatively monitoring plant development. This study proposed a multi-sensor wearable platform for monitoring the growth of plant organs (i.e., stem and fruit) and microclimate (i.e., environmental temperature—T and relative humidity—RH). The platform consists of a custom flexible strain sensor for monitoring growth when mounted on a plant and a commercial sensing unit for monitoring T and RH values of the plant surrounding. A different shape was conferred to the strain sensor according to the plant organs to be engineered. A dumbbell shape was chosen for the stem while a ring shape for the fruit. A metrological characterization was carried out to investigate the strain sensitivity of the proposed flexible sensors and then preliminary tests were performed in both indoor and outdoor scenarios to assess the platform performance. The promising results suggest that the proposed system can be considered one of the first attempts to design wearable and portable systems tailored to the specific plant organ with the potential to be used for future applications in the coming era of digital farms and precision agriculture.
Non Linear Control System for Humanoid Robot to Perform Body Language Movements
Authors: Gomez-Quispe Juan Manuel , Pérez-Zuñiga Gustavo , Arce Diego , Urbina Fiorella , Gibaja Sareli , Paredes Renato , Cuellar Francisco
Published: January 2023

In social robotics, especially with regard to direct interactions between robots and humans, the robotic movements of the body, arms and head must make an adequate displacement to guarantee an adequate interaction, both from a functional and social point of view. To achieve this, the use of closed-loop control techniques that consider the complex nonlinear dynamics and disturbances inherent in these systems is required. In this paper, an implementation of a nonlinear controller for the tracking of trajectories and a profile of speeds that execute the movements of the arms and head of a humanoid robot based on the mathematical model is proposed. First, the design and implementation of the arms and head are initially presented, then the mathematical model via kinematic and dynamic analysis was performed. With the above, the design of nonlinear controllers such as nonlinear proportional derivative control with gravity compensation, Backstepping control, Sliding Mode control and the application of each of them to the robotic system are presented. A comparative analysis based on a frequency analysis, the efficiency in polynomial trajectories and the implementation requirements allowed selecting the non-linear Backstepping control technique to be implemented. Then, for the implementation, a centralized control architecture is considered, which uses a central microcontroller in the external loop and an internal microcontroller (as internal loop) for each of the actuators. With the above, the selected controller was validated through experiments performed in real time on the implemented humanoid robot, demonstrating proper path tracking of established trajectories for performing body language movements.
Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network
Authors: Khant Min , Gouwanda Darwin , Gopalai Alpha A. , Lim King Hann , Foong Chee Choong
Published: January 2023

The inertial measurement unit (IMU) has become more prevalent in gait analysis. However, it can only measure the kinematics of the body segment it is attached to. Muscle behaviour is an important part of gait analysis and provides a more comprehensive overview of gait quality. Muscle behaviour can be estimated using musculoskeletal modelling or measured using an electromyogram (EMG). However, both methods can be tasking and resource intensive. A combination of IMU and neural networks (NN) has the potential to overcome this limitation. Therefore, this study proposes using NN and IMU data to estimate nine lower extremity muscle activities. Two NN were developed and investigated, namely feedforward neural network (FNN) and long short-term memory neural network (LSTM). The results show that, although both networks were able to predict muscle activities well, LSTM outperformed the conventional FNN. This study confirms the feasibility of estimating muscle activity using IMU data and NN. It also indicates the possibility of this method enabling the gait analysis to be performed outside the laboratory environment with a limited number of devices.
Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data
Authors: Raju Viprav B. , Imtiaz Masudul H. , Sazonov Edward
Published: January 2023

Sensor-based food intake monitoring has become one of the fastest-growing fields in dietary assessment. Researchers are exploring imaging-sensor-based food detection, food recognition, and food portion size estimation. A major problem that is still being tackled in this field is the segmentation of regions of food when multiple food items are present, mainly when similar-looking foods (similar in color and/or texture) are present. Food image segmentation is a relatively under-explored area compared with other fields. This paper proposes a novel approach to food imaging consisting of two imaging sensors: color (Red–Green–Blue) and thermal. Furthermore, we propose a multi-modal four-Dimensional (RGB-T) image segmentation using a k-means clustering algorithm to segment regions of similar-looking food items in multiple combinations of hot, cold, and warm (at room temperature) foods. Six food combinations of two food items each were used to capture RGB and thermal image data. RGB and thermal data were superimposed to form a combined RGB-T image and three sets of data (RGB, thermal, and RGB-T) were tested. A bootstrapped optimization of within-cluster sum of squares (WSS) was employed to determine the optimal number of clusters for each case. The combined RGB-T data achieved better results compared with RGB and thermal data, used individually. The mean ± standard deviation (std. dev.) of the F1 score for RGB-T data was 0.87 ± 0.1 compared with 0.66 ± 0.13 and 0.64 ± 0.39, for RGB and Thermal data, respectively.
Impedimetric Bacterial Detection Using Random Antimicrobial Peptide Mixtures
Authors: Stern Bauer Tal , Yakobi Ravit , Hurevich Mattan , Yitzchaik Shlomo , Hayouka Zvi
Published: January 2023

The biosensing of bacterial pathogens is of a high priority. Electrochemical biosensors are an important future tool for rapid bacteria detection. A monolayer of bacterial-binding peptides can serve as a recognition layer in such detection devices. Here, we explore the potential of random peptide mixtures (RPMs) composed of phenylalanine and lysine in random sequences and of controlled length, to form a monolayer that can be utilized for sensing. RPMs were found to assemble in a thin and diluted layer that attracts various bacteria. Faradaic electrochemical impedance spectroscopy was used with modified gold electrodes to measure the charge-transfer resistance (RCT) caused due to the binding of bacteria to RPMs. Pseudomonas aeruginosa was found to cause the most prominent increase in RCT compared to other model bacteria. We show that the combination of highly accessible antimicrobial RPMs and electrochemical analysis can be used to generate a new promising line of bacterial biosensors.
Aptamer-Based Technologies for Parasite Detection
Authors: Brosseau Noah Emerson , Vallée Isabelle , Mayer-Scholl Anne , Ndao Momar , Karadjian Grégory
Published: January 2023

Centuries of scientific breakthroughs have brought us closer to understanding and managing the spread of parasitic diseases. Despite ongoing technological advancements in the detection, treatment, and control of parasitic illnesses, their effects on animal and human health remain a major concern worldwide. Aptamers are single-stranded oligonucleotides whose unique three-dimensional structures enable them to interact with high specificity and affinity to a wide range of targets. In recent decades, aptamers have emerged as attractive alternatives to antibodies as therapeutic and diagnostic agents. Due to their superior stability, reusability, and modifiability, aptamers have proven to be effective bioreceptors for the detection of toxins, contaminants, biomarkers, whole cells, pathogens, and others. As such, they have been integrated into a variety of electrochemical, fluorescence, and optical biosensors to effectively detect whole parasites and their proteins. This review offers a summary of the various types of parasite-specific aptamer-based biosensors, their general mechanisms and their performance.
Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines
Authors: Khan Akif Quddus , Nikolov Nikolay , Matskin Mihhail , Prodan Radu , Roman Dumitru , Sahin Bekir , Bussler Christoph , Soylu Ahmet
Published: January 2023

Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.
Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security
Authors: Ali Sikandar , Abdullah  , Armand Tagne Poupi Theodore , Athar Ali , Hussain Ali , Ali Maisam , Yaseen Muhammad , Joo Moon-Il , Kim Hee-Cheol
Published: January 2023

Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor’s environment, the patient’s environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.
Refractive Index Measurement Using SOI Photodiode with SP Antenna toward SOI CMOS-Compatible Integrated Optical Biosensor
Authors: Satoh Hiroaki , Isogai Koki , Iwata Shohei , Aso Taiki , Hayashi Ryosuke , Takeuchi Shu , Inokawa Hiroshi
Published: January 2023

This paper proposes a new optical biosensor composed of a silicon-on-insulator (SOI) p–n junction photodiode (PD) with a surface plasmon (SP) antenna. When the phase-matching condition between two lateral wavelengths of the diffracted light from the SP antenna and the waveguiding mode in the SOI PD is satisfied, we observe sharp peaks in the spectroscopic light sensitivity. Since the peak wavelength depends on the RI change around the SP antenna corresponding to the phase-matching condition, the SOI PDs with an SP antenna can be applied to the optical biosensor. The RI detection limit is evaluated in the measurements with bulk solutions, and 1.11 × 10−5 RIU (refractive index unit) can be obtained, which is comparable to that of a surface plasmon resonance (SPR) sensor, which is well known as a representative optical biosensor. In addition, the response for intermolecular bonds is estimated by the electromagnetic simulations using the finite-difference time-domain (FDTD) method to clarify its ability to detect biomolecular interactions. The results of this paper will provide new ground for high-throughput label-free biosensing, since a large number of SOI PDs with an SP antenna can be easily integrated on a single chip via an SOI complementary metal-oxide-semiconductor (CMOS) fabrication process.
AFOs Improve Stride Length and Gait Velocity but Not Motor Function for Most with Mild Cerebral Palsy
Authors: White Hank , Barney Brian , Augsburger Sam , Miller Eric , Iwinski Henry
Published: January 2023

Ankle–foot orthoses (AFOs) are prescribed to children with cerebral palsy (CP) in hopes of improving their gait and gross motor activities. The purpose of this retrospective study was to examine if clinically significant changes in gross motor function occur with the use of AFOs in children and adolescents diagnosed with CP (Gross Motor Function Classification System levels I and II). Data from 124 clinical assessments were analyzed. Based on minimum clinically important difference (MCID), 77% of subjects demonstrated an increase in stride length, 45% of subjects demonstrated an increase in walking velocity, and 30% demonstrated a decrease in cadence. Additionally, 27% of the subjects demonstrated increase in gait deviation index (GDI). Deterioration in gait was evident by decreases in walking speed (5% of subjects), increases in cadence (11% of subjects), and 15% of subjects demonstrated decreases in gait deviation index. Twenty-two percent of subjects demonstrated no change in stride lengths and one participant demonstrated a decrease in stride length. However, AFOs improved Gross Motor Function Measure (GMFM) scores for a minority (10%) of children with mild CP (GMFCS level I and II), with 82–85% of subjects demonstrating no change in GMFM scores and 5–7% demonstrating decrease in GMFM scores.
Incorporating a Novel Dual Transfer Learning Approach for Medical Images
Authors: Mukhlif Abdulrahman Abbas , Al-Khateeb Belal , Mohammed Mazin Abed
Published: January 2023

Recently, transfer learning approaches appeared to reduce the need for many classified medical images. However, these approaches still contain some limitations due to the mismatch of the domain between the source domain and the target domain. Therefore, this study aims to propose a novel approach, called Dual Transfer Learning (DTL), based on the convergence of patterns between the source and target domains. The proposed approach is applied to four pre-trained models (VGG16, Xception, ResNet50, MobileNetV2) using two datasets: ISIC2020 skin cancer images and ICIAR2018 breast cancer images, by fine-tuning the last layers on a sufficient number of unclassified images of the same disease and on a small number of classified images of the target task, in addition to using data augmentation techniques to balance classes and to increase the number of samples. According to the obtained results, it has been experimentally proven that the proposed approach has improved the performance of all models, where without data augmentation, the performance of the VGG16 model, Xception model, ResNet50 model, and MobileNetV2 model are improved by 0.28%, 10.96%, 15.73%, and 10.4%, respectively, while, with data augmentation, the VGG16 model, Xception model, ResNet50 model, and MobileNetV2 model are improved by 19.66%, 34.76%, 31.76%, and 33.03%, respectively. The Xception model obtained the highest performance compared to the rest of the models when classifying skin cancer images in the ISIC2020 dataset, as it obtained 96.83%, 96.919%, 96.826%, 96.825%, 99.07%, and 94.58% for accuracy, precision, recall, F1-score, sensitivity, and specificity respectively. To classify the images of the ICIAR 2018 dataset for breast cancer, the Xception model obtained 99%, 99.003%, 98.995%, 99%, 98.55%, and 99.14% for accuracy, precision, recall, F1-score, sensitivity, and specificity, respectively. Through these results, the proposed approach improved the models’ performance when fine-tuning was performed on unclassified images of the same disease.
Thrust Vectoring Control of a Novel Tilt-Rotor UAV Based on Backstepping Sliding Model Method
Authors: Yu Zelong , Zhang Jingjuan , Wang Xueyun
Published: January 2023

In this paper, a control method of a novel tilt-rotor UAV with a blended wing body layout is studied. The novel UAV is capable of vertical take-off and landing and has strong stealth capabilities that can be applied to carrier-borne reconnaissance aircraft. However, the high aspect ratio of blended wing body UAVs leads to a wingtip or oar-tip touchdown problem when adopting the conventional position-attitude control (CPAC) scheme with a large crosswind disturbance. Moreover, when the UAV is subject to interference during reconnaissance, aerial photography, and landing missions, the conventional scheme cannot provide both attitude stability and track accuracy. First, a direct thrust vectoring control (DTVC) scheme is proposed. The control authority of the rotor tilt mechanism was added to enable the decoupling of the attitude and trajectory and to improve the response rate and response bandwidth of the flight trajectory. Second, considering the problems of strong couplings and parameter uncertainties and the nonlinear features and mismatched perturbations that are inevitable in the tilt-rotor, we designed a robust UAV controller based on the backstepping sliding mode control method and determined the stability of the control system through the Lyapunov function. Finally, in the case of crosswire interference during vertical takeoff and landing and the aerial photography missions of the UAV, the numerical simulation of the CPAC scheme and the DTVC scheme was carried out, respectively, and the Monte Carlo random test method was introduced to conduct the statistical test of the landing accuracy. The simulation results show that the DTVC scheme improves the landing accuracy and speed compared to the CAPC scheme and decouples the position control loop from the attitude control loop, finally enabling the UAV to complete the flight control in the VTOL phase.
A Low Cost and Eco-Sustainable Device to Determine the End of the Disinfection Process in SODIS
Authors: Sales-Lérida Diego , Grosso Juan , Martínez-Jiménez Pedro Manuel , Manzano Manuel
Published: January 2023

The lack of safe drinking water is one of the main health problems in many regions of the world. In order to face it, Solar water disinfection (SODIS) proposes the use of transparent plastic containers, which are filled with contaminated water, and exposed to direct sunlight until enough UV radiation is received to inactivate the pathogens. However, a reliable method for determining the end of the disinfection process is needed. Although several approaches have been proposed in the literature for this purpose, they do not strictly accomplish two critical constraints that are essential in this type of project, namely, low cost and sustainability. In this paper, we propose an electronic device to determine when the lethal UV dose has been reached in SODIS containers, which accomplishes both constraints mentioned above: on the one hand, its manufacturing cost is around EUR 12, which is much lower than the price of other electronic solutions; on the other hand, the device is sufficiently autonomous to work for months with small low-cost disposable batteries, thereby avoiding the use of rechargeable batteries, which are considered hazardous waste at the end of their useful life. In our approach, we first analyze different low cost UV sensors in order to select the most accurate one by comparing their response with a reference pattern provided by a radiometer. Then, an electronic device is designed using this sensor, which measures the accumulated UV radiation and compares this value with the lethal UV dose to determine the end of the disinfection process. Finally, the device has been manufactured and tested in real conditions to analyze its accuracy, obtaining satisfactory results.
Long-Term Thermal Comfort Monitoring via Wearable Sensing Techniques: Correlation between Environmental Metrics and Subjective Perception
Authors: Martins Gnecco Veronica , Pigliautile Ilaria , Pisello Anna Laura
Published: January 2023

The improvement of comfort monitoring resources is pivotal for a better understanding of personal perception in indoor and outdoor environments and thus developing personalized comfort models maximizing occupants’ well-being while minimizing energy consumption. Different daily routines and their relation to the thermal sensation remain a challenge in long-term monitoring campaigns. This paper presents a new methodology to investigate the correlation between individuals’ daily Thermal Sensation Vote (TSV) and environmental exposure. Participants engaged in the long-term campaign were instructed to answer a daily survey about thermal comfort perception and wore a device continuously monitoring temperature and relative humidity in their surroundings. Normalized daily profiles of monitored variables and calculated heat index were clustered to identify common exposure profiles for each participant. The correlation between each cluster and expressed TSV was evaluated through the Kendall tau-b test. Most of the significant correlations were related to the heat index profiles, i.e., 49% of cases, suggesting that a more detailed description of physical boundaries better approximates expressed comfort. This research represents the first step towards personalized comfort models accounting for individual long-term environmental exposure. A longer campaign involving more participants should be organized in future studies, involving also physiological variables for energy-saving purposes.
AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
Authors: Bu Fanfeng , Luo Hanjiang , Ma Saisai , Li Xiang , Ruby Rukhsana , Han Guangjie
Published: January 2023

Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption.
Device for Dual Ultrasound and Dry Needling Trigger Points Treatment
Authors: Portilla Gerardo , Montero de Espinosa Francisco
Published: January 2023

Ultrasound is a well-known tool to produce thermal and non-thermal effects on cells and tissues. These effects require an appropriate application of ultrasound in terms of localization and acoustic energy delivered. This article describes a new device that combines ultrasound and dry needling treatments. The non-thermal effects of ultrasound should locally amplify the needle’s effects. The ultrasound transducer can mechanically rotate in 3D space to align itself in the direction of the needle. The transducer electronically focuses the acoustic pressure automatically on the needle tip and its surroundings. A computer, using graphical interface software, controls the angulation of the array and the focus position.
Strain Engineering of ZrO2@TiO2 Core@shell Nanoparticle Photocatalysts
Authors: Swadener John G.
Published: January 2023

TiO2 photocatalysts can provide carbon-capture utilization and storage by converting atmospheric CO2 to green hydrogen, but the efficiency of the current photocatalysts is still too low for economical usage. Anatase TiO2 is effective in transferring the electrons and holes produced by the photoelectric effect to reactants because of its oxygen-terminated surfaces. However, the anatase TiO2 bandgap is 3.2 eV, which requires photons with wavelengths of 375 nm or less to produce electron–hole pairs. Therefore, TiO2 is limited to using a small part of the solar spectrum. Strain engineering has been used to design ZrO2@TiO2 core@shell structures with large strains in the TiO2 shell, which reduces its bandgap but maintains octahedral facets for charge separation and oxygen-terminated surfaces for the catalysis of reactants. Finite element analysis shows that shell thicknesses of 4–12 nm are effective at obtaining large strains in a large portion of the shell, with the largest strains occurring next to the ZrO2 surface. The c-axis strains for 4–12 nm shells are up to 7%. The strains reduce the bandgap in anatase TiO2 up to 0.35 eV, which allows for the use of sunlight with wavelengths up to 421 nm. For the AM 1.5 standard spectrum, electron–hole pair creation in 4 nm thick and 10 nm thick TiO2 shells can be increased by a predicted 25% and 23%, respectively. The 10 nm thick shells provide a much larger volume of TiO2 and use proportionally less ZrO2. In addition, surface-plasmon resonators could be added to further extend the usable spectrum and increase the production of electron–hole pairs many-fold.
Dimensioning and Lifetime Prediction Model for a Hybrid, Hydrogen-Based Household PV Energy System Using Matlab/Simulink
Authors: Möller Marius C. , Krauter Stefan
Published: January 2023

This paper presents a model of an energy system for a private household extended by a lifetime prognosis. The energy system was designed for fully covering the year-round energy demand of a private household on the basis of electricity generated by a photovoltaic (PV) system, using a hybrid energy storage system consisting of a hydrogen unit and a lithium-ion battery. Hydrogen is produced with a Proton Exchange Membrane (PEM) electrolyser by PV surplus during the summer months and then stored in a hydrogen tank. Mainly during winter, in terms of lack of PV energy, the hydrogen is converted back into electricity and heat by a fuel cell. The model was created in Matlab/Simulink and is based on real input data. Heat demand was also taken into account and is covered by a heat pump. The simulation period is a full year to account for the seasonality of energy production and demand. Due to high initial costs, the longevity of such an energy system is of vital interest. Therefore, this model was extended by a lifetime prediction in order to optimize the dimensioning with the aim of lifetime extension of a hydrogen-based energy system. Lifetime influencing factors were identified on the basis of a literature review and were integrated in the model. An extensive parameter study was performed to evaluate different dimensionings regarding the energy balance and the lifetime of the three components, electrolyser, fuel cell and lithium-ion battery. The results demonstrate the benefits of a holistic modelling approach and enable a design optimization regarding the use of resources, lifetime and self-sufficiency of the system.
Simplified Calculation of Tsol Based on Dynamic Numerical Simulation of Tsky in Diverse Climates in China
Authors: Chen Jie , Fan Yue , Wang Menghan
Published: January 2023

An accurate calculation of sol-air temperature (Tsol) is very important for urban environments and building energy consumption. There are various methods that can be used to calculate Tsol by considering sky radiation effects. Climate conditions are vital factors affecting sky temperature (Tsky). In this paper, in order to select an appropriate calculation method to determine long-wave radiation, a theoretical analysis was carried out based on the effect of Tsky on the thermal gain of building envelopes due to long-wave radiation. Typical annual meteorological data were selected to calculate Tsol for 10 meteorological stations covering five building thermal zones in China. The application of the Tsol model was studied using MBE as the measurement standard, and a linear regression equation for the calorific value of the envelope obtained via the Tsky estimation method and the Tsky dynamic calculation method was established. The results show that relative humidity is the key meteorological factor that affects the application of the Tsol model and that the Tsky dynamic calculation should be used to calculate long-wave radiation in regions with low relative humidity. A thermal correction equation for buildings was obtained for use in areas lacking meteorological data and to provide a basis for sustainable building design.
Social Commerce and Purchase Intention: A Brief Look at the Last Decade by Bibliometrics
Authors: Dincer Caner , Dincer Banu
Published: January 2023

Over the last decade, the development of smartphones, social networking sites, and applications led to a rise in social commerce, a new way for companies and individuals to carry on a trade. The s-commerce trend is based mostly on information exchange and social connections, and their effect on users’ purchase intention. This study seeks to observe trends in the literature on social commerce while investigating its interplay with purchase intention using bibliometric analysis. This analysis is one of the best tools to summarize the research in the field and to designate the trends and future directions. However, to eliminate data duplication and the cleaning process, only the ScienceDirect database is used with “social commerce” and “purchase intention” as keywords, providing us with 71 studies for the period 2013–2022. This study sorts these articles according to the following bibliographic indicators: year of publication, journal with most published research, authors, language, keywords, and citation frequency. The general themes and methods of the papers in the database are also investigated to better cover the topic. This analysis provides insight for this line of research into purchase intention in social commerce.
Population Dynamics and Seasonal Patterns of Chironomus plumosus (Diptera, Chironomidae) in the Shallow Lake Trasimeno, Central Italy
Authors: Pallottini Matteo , Pagliarini Sarah , Catasti Marianna , La Porta Gianandrea , Selvaggi Roberta , Gaino Elda , Spacone Leonardo , Di Giulio Alessandro Maria , Ali Arshad , Goretti Enzo
Published: January 2023

Field sampling of littoral macrobenthos of the shallow Lake Trasimeno was conducted along 17 years (2005–2021) on 129 different occasions. This long-term field study deepens the knowledge concerning the life cycle of Chironomus plumosus (Diptera), the main responsible for summer chironomid swarms that adversely affect human littoral activities, providing useful information for its management. About 108,000 macrobenthic specimens were collected, belonging to Oligochaeta (Naididae) (62%), Diptera (Chironomidae) (37%), and only 1.5% to other invertebrate taxa. Eighteen chironomid taxa were found. The trend of chironomid density was not affected by C. plumosus, which showed a maximum increase in September. This peak is justified by the presence of large swarms of C. plumosus in late August in which the populations of the central area of Lake Trasimeno consistently participate. The larval density of this species did not increase over the 17 years. A detailed analysis of the sampled larvae and adult biomass catches from 2017 to 2020 reveals that four annual swarmings occurred: in April, July, August, and September–October. The water temperature remains higher than 20 °C during the night hours from the end of May to mid-September, strengthening the hypothesis of the three midge swarming cycles in the summer period until early autumn.
Impact of Environmental Economic Transformation Based on Sustainable Development on Financial Eco-Efficiency
Authors: Yin Limei , Liu Jia
Published: January 2023

The production and life of human beings are inseparable from the natural environment, and the current economic transformation is based on the sustainable development of the environment. However, the current environmental economic transformation lacks a corresponding evaluation model, so this paper aimed to explore the path of environmental economic transformation and analyze the impact of environmental economic transformation on financial eco-efficiency. Aiming at the transformation of environmental economy, this paper analyzed the relationship between the environmental quality and the transformation path and made a detailed analysis of the dynamic and static transformation process. After understanding the path of environmental economic transformation, this paper established a model to analyze the impact of eco-efficiency. In terms of indicators, this paper selected four indicators of environmental economic transformation: return on assets, gross margin of sales, period expense rate, and total asset turnover. Through data analysis, this paper discussed the impact of these four indicators on financial eco-efficiency. The experimental results show that the comprehensive coefficient of environmental and economic transformation indicators is 1.325 (p < 0.001). This shows that the environmental economic transformation has a significant positive correlation with the financial eco-efficiency, that is to say, a good environmental economic transformation can increase the financial eco-efficiency index.
The Effect of Good Agricultural Practices on the Technical Efficiency of Chili Production in Thailand
Authors: Krasachat Wirat
Published: January 2023

While recognition of the positive influence of good agricultural practices (GAP) on reducing negative externalities due to conventional farming and providing more export opportunities is growing, there is some doubt about the effects of GAP on the economic performance of chili farms. In this regard, this study’s principal objectives are to assess the impact of GAP and to examine farm-specific and environmental factors regarding the technical efficiency (TE) of chili farms in Thailand. This study employed a stochastic meta-frontier input distance function to measure and explore the effects of farm-specific and environmental factors on TE using 2018 farm-level survey data from Thai chili farms. The sample of 100 farms includes GAP and non-GAP farms. The empirical results highlight three critical findings. First, there is confirmation that GAP positively influence the TE of chili farms. Second, family labor intensity and small farm size also positively impact the TE of farms. At the same time, completion of a technical training course positively affects the TE of non-GAP farms only. Finally, education, experience, training courses, and crop diversification negatively affect the TE of GAP farms only. Thus, policymakers need to focus on GAP adoption and farm-specific factors to promote the sustainable development of Thai chili farms.
Performance Measurement Systems in Continuous Improvement Environments: Obstacles to Their Effectiveness
Authors: Cunha Flávio , Dinis-Carvalho José , Sousa Rui M.
Published: January 2023

The implementation of an effective performance measurement system (PMS) is essential in organizations seeking continuous improvement and sustainability, especially in the context of Industry 4.0. However, a number of barriers/obstacles hampers this effectiveness. For example, the inclusion of performance indicators without practical use, or presenting absurd results, are frequent in the industry, leading people to distrust and devalue such systems. The main research question in this study is: What are the main obstacles to effective performance measurement systems in organizations? These obstacles will be identified and categorized, and some conditions required to eliminate/mitigate them will be explored. A systematic literature review is carried out, according to the PRISMA methodology, to identify the most frequent factors referred to as obstacles to PMS effectiveness, due to their negative influence on the implementation, use or maintenance of these systems. This review resulted in the identification of 175 obstacles that were then classified into 19 types of obstacles divided into 6 categories, whose definition was inspired by the 6M approach usually associated with Ishikawa diagrams. Then, the relationships between the obstacles are explored and investigated in order to identify interdependencies that may further influence the effectiveness of the PMS. Solutions and methodologies that can help mitigate/eliminate their impact on PMS effectiveness are also identified.
Increasing Geoheritage Awareness through Non-Formal Learning
Authors: Coratza Paola , Vandelli Vittoria , Ghinoi Alessandro
Published: January 2023

Non-formal learning can have a crucial role in increasing citizens’ literacy to geoscience providing the opportunity to raise the public profile of geology and geomorphology. Starting from these remarks, the project presented here is one of the first attempts, at national level, aimed at achieving the territorial upgrading based on geoheritage enhancement. The project started thanks to a bottom-up input and involved the collaboration between scholars and local administrations and stakeholders for the valorization of a fluvial area within the Municipality of Castellarano (Emilia Apennines, Northern Italy). To achieve this aim of non-formal learning activities, based on the interpretation of the geoheritage, have been implemented. In fact, the investigated area includes valuable geological and geomorphological features which have been used, in the frame of the project here presented, to promote local geodiversity and geotourism. In particular, three geosites of regional significance were considered for the creation of EarthCaches, interpretative panels and guided excursions. Interpretative contents were designed to be educational, providing accurate but non-technical explanations. Attention was given in including illustrations playing an important role in the learning process. The results revealed that the implemented activities positively contribute to raising public awareness on the value of geoheritage.
Preparation of an Environmentally Friendly Rice Seed Coating Agent and Study of Its Mechanism of Action in Seedlings
Authors: Fang Jinfu , Zeng Defang , Xu Tian
Published: January 2023

Traditional rice seed coating agents (TRSCA) contain toxic components that pollute the environment and threaten human health. The use of safe, high-efficiency, and environmentally friendly seed coating agents is vital for environmental protection. We studied the production of a new, environmentally friendly rice seed coating agent and its mechanism at the seedling stage. We assess the difference in mechanism of action between the new seed coating agent and the representative TRSCAs on the market through laboratory and field experiments. Following the application of the new seed coating agent, bakanae disease was controlled at a rate of over 80.5% and insect pest feeding was controlled at a rate of 81%. More importantly, the LC50 value was 10 times higher than following TRSCA treatment. The coating agent can enhance the activity of plant protective enzymes (peroxidase [POD], catalase [CAT], and superoxide dismutase [SOD]) and the activity of rice seedling roots. The coating agent is antibacterial, disease preventative, deworming, safe, and environmentally protective, and results in the production of strong seedlings, suggesting it would be a good alternative to TRSCA. Our analysis found that the control effect of the seed coating on rice seedling disease was likely achieved by activating the plant protection enzymes (e.g., POD, CAT, and SOD). The effect of the coating agent on rice is likely achieved through increased root activity and the improvement of the rhizosphere micro-ecological system.
The Impact of Clothing E-Store Image on Intention Based on Search and Purchase Phases: From the Perspective of Sustainable Marketing
Authors: Jiang Liyuan , Li Quanxi , Wu Xiaoding
Published: January 2023

With the continuous emergence of global warming and excessive waste of resources, especially in the clothing industry, sustainable clothing has become a fast-moving consumer goods trend. As a necessity of life, clothing often sensitively reflects the fashion, the characteristics of the times and, for young people, their lifestyle. Intelligent, cultural and sustainable clothing has become the mainstream development trend of the clothing industry. Based on the theory of reasoned action, this research takes online clothing stores as the research object and constructs the effect model between e-store image, consumer attitude and intention, and verifies the impact of e-store image on consumers’ intention in the two important stages of information search and purchase. By distributing online questionnaires to consumers of online clothing stores, 823 questionnaires were finally effectively recovered. The hypothesis was verified using a multi-statistical analysis and structural equation model. The findings showed that for young consumers, in the information search stage, the three dimensions of e-store image, which are information, atmosphere and convenience, positively affect consumers’ search intention. Meanwhile, in the purchase stage, the three dimensions of e-store image, which are enjoyment, uncertainty and service, have a significant impact on consumers’ purchase intention. Particularly, consumer attitude plays a mediating role between different dimensions and intentions. According to the constructed consumer attitude model, we should propose these sustainable marketing suggestions for online clothing stores: First of all, online clothing store operators should improve the atmosphere, convenience and information image of electronic stores, establish a sustainable image, and enhance young consumer’s identity. Secondly, using multiple marketing methods, we can establish an unimpeded one-to-one interaction with young customers through the current popular live broadcast marketing, improve the enjoyment of stores, more specifically implement marketing strategies, and strengthen the sense of sustainability.
Determinants of Intentions to Use Digital Mental Healthcare Content among University Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot
Authors: Park Daniel Y. , Kim Hyungsook
Published: January 2023

Depression is a worldwide health issue to which various physical, psychological, and social health problems are attributable. To address the issue through the promotion of digital mental healthcare content use, this study examines factors influencing people’s intentions to use the content, guided by the technology acceptance model and uses and gratifications theory. A total of 278 students and faculty/staff members at a Korean university tried using a digital mental healthcare content (e.g., artificial intelligence chatbot content) called MyMentalPocket and completed a survey questionnaire associated with their perceptions of the content. Participants’ depression levels, perceived usefulness, and parasocial interactions emerged as significant and positive factors influencing people’s intentions to use MyMentalPocket. Female gender, younger age, and specific motives for depression-related digital technology use (i.e., communication and emotional support, information- and guidance-seeking, and habitual entertainment-seeking motives) emerged as significant and positive factors influencing parasocial interactions. Parasocial interactions and perceived ease of use emerged as significant and positive factors influencing perceived usefulness. The findings from this study imply the utility of AI chatbots as a way to help people, especially females and younger people with depression and interpersonal difficulties, to utilize and benefit from digital mental healthcare content for depression management.