bluuebunny/crossref_metadata_2025 huggingface dataset:
Seasonal Differences in Leaf Photoprotective Potential between Adults and Juveniles of Two Mediterranean Perennials with Distinct Growth Forms: A Comparative Field Study
Authors: Chondrogiannis Christos , Kotsi Kassiani , Grammatikopoulos George , Petropoulou Yiola
Published: August 2023

The photosynthetic differences between adult and juvenile Mediterranean plants were previously studied under field conditions, yet the corresponding differentiation of their photoprotective efficiency has not been sufficiently investigated. The present study aims to examine possible differences in the photoprotective potential between adults and juveniles of two native Mediterranean plants with distinct growth forms. Thus, the seasonal variations in individual carotenoids, electron transport rate (ETR), and non-photochemical quenching (NPQ) were monitored in fully exposed mature leaves from adults and juveniles of the winter deciduous tree Cercis siliquastrum L. and the evergreen sclerophyllous shrub Nerium oleander L. All plants were grown under apparently similar field conditions. In both species, juveniles displayed substantially lower ETR and increased NPQ values than adults in spring, with the differences intensifying during summer drought and diminishing in autumn. Concomitantly, juveniles showed significantly higher chlorophyll-based total carotenoids in spring and summer mainly due to the higher investment in xanthophyll cycle components (VAZ), in combination with an increased mid-day de-epoxidation state (DEPS) and partial retention of zeaxanthin in the dark. In N. oleander, although ETR was lower in juveniles during winter, NPQ was extremely low in both ages. In conclusion, juveniles exhibit enhanced photoprotection potential, especially in the summer, due to their reduced photochemical capacity. The photosynthetic superiority of adults during the favorable spring period may be attributed to the needs of the co-existing reproductive effort.
Predicting the Responses of Functional Leaf Traits to Global Warming: An In Situ Temperature Manipulation Design Using Iris pumila L.
Authors: Manitašević Jovanović Sanja , Hočevar Katarina , Vuleta Ana , Tucić Branka
Published: August 2023

Phenotypic plasticity is widely acknowledged as one of the most common solutions for coping with novel environmental conditions following climate change. However, it is less known whether the current amounts of trait plasticity, which is sufficient for matching with the contemporary climate, will be adequate when global temperatures exceed historical levels. We addressed this issue by exploring the responses of functional and structural leaf traits in Iris pumila clonal individuals to experimentally increased temperatures (~1.5 °C) using an open top chamber (OTC) design. We determined the phenotypic values of the specific leaf area, leaf dry matter content, specific leaf water content, and leaf thickness in the leaves sampled from the same clone inside and outside of the OTC deployed on it, over seasons and years within two natural populations. We analyzed the data using a repeated multivariate analysis of variance, which primarily focusses on the profiles (reaction norms (RNs)) of a variable gathered from the same individual at several different time points. We found that the mean RNs of all analyzed traits were parallel regardless of experienced temperatures, but differed in the level and the shape. The populations RNs were similar as well. As the amount of plasticity in the analyzed leaf trait was adequate for coping with elevated temperatures inside the OTCs, we predict that it will be also sufficient for responding to increased temperatures if they exceed the 1.5 °C target.
CRISPR/Cas Technology Revolutionizes Crop Breeding
Authors: Tang Qiaoling , Wang Xujing , Jin Xi , Peng Jun , Zhang Haiwen , Wang Youhua
Published: August 2023

Crop breeding is an important global strategy to meet sustainable food demand. CRISPR/Cas is a most promising gene-editing technology for rapid and precise generation of novel germplasm and promoting the development of a series of new breeding techniques, which will certainly lead to the transformation of agricultural innovation. In this review, we summarize recent advances of CRISPR/Cas technology in gene function analyses and the generation of new germplasms with increased yield, improved product quality, and enhanced resistance to biotic and abiotic stress. We highlight their applications and breakthroughs in agriculture, including crop de novo domestication, decoupling the gene pleiotropy tradeoff, crop hybrid seed conventional production, hybrid rice asexual reproduction, and double haploid breeding; the continuous development and application of these technologies will undoubtedly usher in a new era for crop breeding. Moreover, the challenges and development of CRISPR/Cas technology in crops are also discussed.
Evaluation of the Phytoremediation Potential of the Sinapis alba Plant Using Extractable Metal Concentrations
Authors: Vasilache Nicoleta , Diacu Elena , Cananau Sorin , Tenea Anda Gabriela , Vasile Gabriela Geanina
Published: August 2023

Testing the feasibility of soil phytoremediation requires the development of models applicable on a large scale. Phytoremediation mechanisms include advanced rhizosphere biodegradation, phytoaccumulation, phytodegradation, and phytostabilization. The aim of this study was to evaluate the phytoremediation potential of the Sinapis alba. Identification of the factors influencing the extraction process of metals from contaminated soils in a laboratory system suitable for evaluating the phytoavailability of these metals in three solutions (M1-CaCl2, M2-DTPA, and M3-EDTA) included the following: distribution of metals in solution (Kd), soil properties and mobile fractions (SOC, CEC, pH), response surface methodology (RSM), and principal component analysis (PCA). The evaluation of the phytoremediation potential of the Sinapis alba plant was assessed using bioaccumulation coefficients (BACs). The accumulation of heavy metals in plants corresponds to the concentrations and soluble fractions of metals in the soil. Understanding the extractable metal fractions and the availability of metals in the soil is important for soil management. Extractable soluble fractions may be more advantageous in total metal content as a predictor of bioconcentrations of metals in plants. In this study, the amount of metal available in the most suitable extractors was used to predict the absorption of metals in the Sinapis alba plant. Multiple regression prediction models have been developed for estimating the amounts of As and Cd in plant organs. The performance of the predictive models generated based on the experimental data was evaluated by the adjusted coefficient of determination (aR2), model efficiency (RMSE), Durbin–Watson (DW) test, and Shapiro–Wilk (SW) test. The accumulation of the analyzed metals followed the pattern Root > Pods > Leaves > Seeds, stems > Flowers for As and Leaves > Root > Stem > Pods > Seeds > Flowers for Cd in soil contaminated with different metal concentrations. The obtained results showed a phytoremediation potential of the Sinapis alba plant.
Laser-Induced Fluorescence for Monitoring Environmental Contamination and Stress in the Moss Thuidium plicatile
Authors: Truax Kelly , Dulai Henrietta , Misra Anupam , Kuhne Wendy , Fuleky Peter , Smith Celia , Garces Milton
Published: August 2023

The ability to detect, measure, and locate the source of contaminants, especially heavy metals and radionuclides, is of ongoing interest. A common tool for contaminant identification and bioremediation is vegetation that can accumulate and indicate recent and historic pollution. However, large-scale sampling can be costly and labor-intensive. Hence, non-invasive in-situ techniques such as laser-induced fluorescence (LIF) are becoming useful and effective ways to observe the health of plants through the excitation of organic molecules, e.g., chlorophyll. The technique presented utilizes images collected of LIF in moss to identify different metals and environmental stressors. Analysis through image processing of LIF response was key to identifying Cu, Zn, Pb, and a mixture of the metals at nmol/cm2 levels. Specifically, the RGB values from each image were used to create density histograms of each color channel’s relative pixel abundance at each decimal code value. These histograms were then used to compare color shifts linked to the successful identification of contaminated moss samples. Photoperiod and extraneous environmental stressors had minimal impact on the histogram color shift compared to metals and presented with a response that differentiated them from metal contamination.
Effect of Co-Application of Azospirillum brasilense and Rhizobium pisi on Wheat Performance and Soil Nutrient Status under Deficit and Partial Root Drying Stress
Authors: Alhammad Bushra Ahmed , Zaheer Muhammad Saqlain , Ali Hafiz Haider , Hameed Akhtar , Ghanem Kholoud Z. , Seleiman Mahmoud F.
Published: August 2023

Water management techniques are improving at the farm level, but they are not enough to deal with the limited availability of water and increased crop yields. Soil microbes play a vital role in nitrogen fixation, improving soil fertility and enhancing plant growth hormones under drought conditions. Therefore, this study was conducted to investigate the impact of water management combined with Azospirillum brasilense and Rhizobium pisi on wheat crop productivity and soil properties in dry regions. Three water management techniques were compared, normal irrigation as a control (C), deficit irrigation (DI), and partial root drying irrigation (PRD), together with the interaction of plant-growth-promoting rhizobacteria (PGPR). Experiments were conducted with six treatments in total: T1 = C + No PGPR, T2 = C + PGPR, T3 = DI + No PGPR, T4 = DI + PGPR, T5 = PRD + No PGPR, and T6 = PRD + PGPR. The highest grain yield was achieved in the control irrigation treatment using seeds inoculated with rhizobacteria, followed by control treatment without any inoculation, and the lowest was recorded with deficit irrigation without rhizobacteria inoculated in the seeds. However, PRD irrigation resulted in significantly higher plant growth and grain yield than the DI treatment. PGPR inoculation combined with PRD resulted in a 22% and 20% higher number of grains per spike, a 19% and 21% higher grain yield, and a 25% and 22% higher crop growth rate compared to rhizobacteria inoculation combined with the DI system in 2021-22 and 2022-23, respectively. This increase was due to the higher production of growth hormones and higher leaf area index under water-limited conditions. A greater leaf area index leads to a higher chlorophyll content and higher food production for plant growth.
Government-Funded Development of Innovative Physical Technologies for Sustainable Agriculture and Food Production in Rural Germany through a University–Business Alliance Formation
Authors: Garbe Leif-Alexander , Glaß Sebastian , Wald Florian , Hellmann Andrea , Weltmann Klaus-Dieter , Sawade Hans , Schultz Fabien
Published: August 2023

Purpose: The alliance project “Physics for Food” aims to invent new physical technologies for sustainable agriculture and food production in Mecklenburg-Western Pomerania, an agrarian region in northern Germany. This article may serve as an example of good practice for regional collaboration and funding acquisition between academia and the industry, especially SMEs, that may be replicated in other rural contexts. Approach: The project consortium consists of a triple-helix setting of scientists from university and research institutes as well as technology suppliers, seed producers, and farmers. The German Federal Ministry of Education and Research (BMBF) funds the project in a special program called “WIR!” that addresses innovation and structural transformation of lagging regions in Germany. Findings: The program encourages development of regional innovation concepts and supports confederations that cross disciplinary, industrial, institutional, and administrative boundaries and thrive on civic involvement. Today already, there is huge potential and an increasing demand for the development and the supply of novel non-chemical alternatives in plant and storage protection in agriculture and food production. Originality: The project aims to lay the foundation for startup companies based in the region to act as innovative technology providers and to create jobs in the region while making the new technologies available nationally and internationally. The application of physical methods will decrease costs, conserve resources, and eventually contribute to protecting the environment.
The Effect of Silane Acrylate Containing Ethylene Glycol Chains on the Adhesive Performance and Viscoelastic Behavior of Acrylic Pressure-Sensitive Adhesives for Flexible Displays
Authors: Seok Woong Cheol , Leem Jong Tae , Song Ho Jun
Published: August 2023

In this study, novel silane acrylates, such as diethylene glycol diacrylate (DEGDA) and tetraethylene glycol diacrylate (TEGDA), containing ethylene glycol chains were synthesized and introduced into acrylic pressure-sensitive adhesives (PSAs) to regulate their peel strength and rheological properties. The synthesized silane acrylates effectively improved the cohesion and adhesive properties of the acrylic PSAs, even with only 1 wt% addition. In addition, the glass transition temperature and flexibility of acrylic PSAs were also affected by the increase in free volume induced by ethylene glycol chains. The silane acrylates also improved the viscoelasticity of the acrylic PSAs, which exhibited excellent recovery (62–96%) and stress relaxation (>90%) properties owing to the increased elasticity. Additionally, the acrylic PSAs prepared with the silane acrylates showed excellent optical properties (transmittance ≥ 90%, haze ≤ 1%) and exhibited behavior suitable for application in flexible displays from a comprehensive perspective.
Analysis of the Dynamic Cushioning Property of Expanded Polyethylene Based on the Stress–Energy Method
Authors: Xing Yueqing , Sun Deqiang , Chen Guoliang
Published: August 2023

This paper aimed to experimentally clarify the dynamic crushing performance of expanded polyethylene (EPE) and analyze the influence of thickness and dropping height on its mechanical behavior based on the stress–energy method. Hence, a series of impact tests are carried out on EPE foams with different thicknesses and dropping heights. The maximum acceleration, static stress, dynamic stress and dynamic energy of EPE specimens are obtained through a dynamic impact test. Then, according to the principle of the stress–energy method, the functional relationship between dynamic stress and dynamic energy is obtained through exponential fitting and polynomial fitting, and the cushion material constants a, b and c are determined. The maximum acceleration-static stress curves of any thickness and dropping height can be further fitted. By the equipartition energy domain method, the range of static stress can be expanded, which is very fast and convenient. When analyzing the influence of thickness and dropping height on the dynamic cushioning performance curves of EPE, it is found that at the same drop height, with the increase of thickness, the opening of the curve gradually becomes larger. The minimum point on the maximum acceleration-static stress curve also decreases with the increase of the thickness. When the dropping height is 400 mm, compared to foam with a thickness of 60 mm, the tested maximum acceleration value of the lowest point of the specimen with a thickness of 40 mm increased by 45.3%, and the static stress is both 5.5 kPa. When the thickness of the specimen is 50 mm, compared to the dropping height of 300 mm, the tested maximum acceleration value of the lowest point of the specimen with a dropping height of 600 mm increased by 93.3%. Therefore, the dynamic cushioning performance curve of EPE foams can be quickly obtained by the stress–energy method when the precision requirement is not high, which provides a theoretical basis for the design of cushion packaging.
Stability Study of Selected Coxibs Used in the Treatment of Rheumatoid Diseases in Various Drug Combinations
Authors: Gumułka Paweł , Dąbrowska Monika , Starek Małgorzata
Published: August 2023

Coxibs are a group of non-steroidal anti-inflammatory drugs (NSAIDs), selective cyclooxygenase 2 inhibitors, characterized by a much lower gastrotoxicity compared to classic NSAIDs. They are often used in conjunction with other drugs, which greatly increases the likelihood of adverse drug interactions. The presented study analyzed the degradation rate of celecoxib and cimicoxib in solutions under the influence of other medicinal substances at different temperatures. For this purpose, triple-drug mixtures were prepared, consisting of coxib and eleven different commonly used drugs (paracetamol, ketoprofen, diclofenac, acetylsalicylic acid, ibuprofen, meloxicam, tramadol, doxycycline, bisoprolol, and caffeine). Then, the mixtures were incubated at two temperatures. Within the time specified by the research plan, further aliquots of the mixtures were subjected to a chromatographic analysis. Separation was conducted on HPTLC F254 silica gel chromatographic plates as a stationary phase, using chloroform: acetone: toluene as a mobile phase, and was detected densitometrically at wavelengths of 254 nm. The percentage changes in the tested coxibs content, depending on the time and conditions of incubation, were presented. Based on the obtained data, the basic kinetic parameters of the degradation processes were determined. The celecoxib and cimicoxib showed a relatively high durability in changing environmental conditions. It was observed that the rate of decomposition of cimicoxib and celecoxib in the tested mixtures was different and depended on the temperature and presence of other components, with cimicoxib turning out to be a more stable compound.
Retrospective and Prospective Approaches to Christian Education in Church of Christ Schools in Zimbabwe
Authors: Masengwe Gift , Dube Bekithemba
Published: August 2023

This article explores the past, present, and future of Christian education (CE) in the Church of Christ in Zimbabwe (COCZ) schools under the Dadaya, Mashoko, and Chidamoyo clusters. It highlights the retrospective and prospective aspects of CE within the church by emphasizing the adaptability, collaboration, and utilization of contemporary tools and resources to effectively carry out the mission of CE in Zimbabwean schools. It acknowledges the strong historical foundation of COCZ schools in providing biblical teachings, moral formation, and faith integration. However, it also highlights the challenges faced by the COCZ in maintaining this foundation posed by church politics and oppressive family ministries. This study assesses the patterns, dynamics, and consequences of intentional CE influenced by the COCZ’s ethical and moral principles, commonly known as the Restoration Movement principles or the Stone–Campbell theories of “restoring the church to its first century forms”. It recognizes the impact of COCZ school education on the development of church leaders as well as civil and secular leaders from COCZ-mission school graduates. However, it acknowledges that the historical foundation has been gradually fading in the post-missionary era. Looking ahead, this article emphasizes the need for a comprehensive and holistic approach to CE that addresses all aspects of individuals’ well-being in that church. This includes adapting to societal changes, developing CE-conscious leaders, engaging in community outreach, embracing digital tools and resources, and fostering collaborative networks with other CE-conscious church schools and institutions. This article suggests that by embracing these prospects, the COCZ can enhance its commitment to CE and positively impact the lives of its members and the wider community at its mission stations. Using WhatsApp interviews for data collection, it discusses these prospects by focusing on transformative leadership, embracing diversity, effective communication, and addressing power dynamics to facilitate the church’s mission and vision in Zimbabwe.
Shaping Urban Religious Topography in the Iberian Peninsula between the Fourth and Sixth Centuries: “Coopetitive” Rivalry and Social Power
Authors: López-Gómez José Carlos
Published: August 2023

This study analyses the reshaping of the religious landscape of the cities of Hispania, from the disappearance of polytheistic sanctuaries to the construction of Christian churches, from the fourth to the middle of the sixth centuries CE. The focus is placed on the agents who financed these complexes and the motivations behind their euergetic activities. The study highlights that the configuration of places of worship in the urban landscape was a contingent process often dependent on individual (layperson) initiative, challenging the widespread assumption that the Church and its bishops were the main promoters of urban redevelopment in Hispanic cities during this period.
Tracking Low-Frequency Variations in Land–Sea Water Mass Redistribution during the GRACE/GRACE-FO Era
Authors: Deng Shanshan , Jian Zhenlong , Liu Yuxin , Yi Chushun , Chen Yi , Zhang Wenxi
Published: August 2023

Climate change has caused a widespread deduction in terrestrial water storage (TWS), leading to ocean water mass gains and sea level rises. A better understanding of how the land–sea water mass has been redistributed can help with the scientific response to climate change. However, there are few studies investigating the roles of the different physical processes involved in low-frequency land–sea water mass redistribution on a global scale. To address this issue, in this study, a comprehensive investigation was carried out with respect to the globally distributed key factors causing low-frequency ocean mass anomalies during the period 2004–2021. Global water mass redistribution data, derived from GRACE/GRACE-FO satellite gravity and surface wind and sea-surface temperature data from ERA5 reanalysis, were employed, and the empirical orthogonal function, maximum covariance analysis, and sea-level equation approaches were used. The results show that the long-term trend and decadal-like fluctuation are two major components of the low-frequency land–sea water mass redistribution. The wind-forcing dynamic processes significantly drive the anomalies near the North Indian Ocean, North Atlantic Ocean, South Pacific Ocean, and some marginal seas, where variance explanations range from 30% to 97%. After removing the ocean dynamics, the residual ocean mass anomaly is mostly explained by sea-level fingerprints (SLFs), especially in the open ocean. The 25th, 50th, and 75th percentiles of the SLF-explained variances in all ocean grids are 59%, 72%, and 82%, respectively. Some non-negligible noise, located in seismic zones, was also found, suggesting the misestimation of seafloor deformation resulting from earthquakes in the GRACE/GRACE-FO data processing. These findings may improve our understanding of the long-term anomalies in regional and global sea levels.
FCAE-AD: Full Convolutional Autoencoder Based on Attention Gate for Hyperspectral Anomaly Detection
Authors: Wang Xianghai , Wang Yihan , Mu Zhenhua , Wang Ming
Published: August 2023

Recently, the methods based on the autoencoder reconstruction background have been applied to the area of hyperspectral image (HSI) anomaly detection (HSI-AD). However, the encoding mechanism of the autoencoder (AE) makes it possible to treat the anomaly and the background indistinguishably during reconstruction, which can result in a small number of anomalous pixels still being included in the acquired reconstruction background. In addition, the problem of redundant information in HSIs also exists in reconstruction errors. To this end, a fully convolutional AE hyperspectral anomaly detection (AD) network with an attention gate (AG) connection is proposed. First, the low-dimensional feature map as a product of the encoder and the fine feature map as a product of the corresponding decoding stage are simultaneously input into the AG module. The network context information is used to suppress the irrelevant regions in the input image and obtain the significant feature map. Then, the features from the AG and the deep features from upsampling are efficiently combined in the decoder stage based on the skip connection to gradually estimate the reconstructed background image. Finally, post-processing optimization based on guided filtering (GF) is carried out on the reconstruction error to eliminate the wrong anomalous pixels in the reconstruction error image and amplify the contrast between the anomaly and the background.
A Review on UAV-Based Applications for Plant Disease Detection and Monitoring
Authors: Kouadio Louis , El Jarroudi Moussa , Belabess Zineb , Laasli Salah-Eddine , Roni Md Zohurul Kadir , Amine Ibn Dahou Idrissi , Mokhtari Nourreddine , Mokrini Fouad , Junk Jürgen , Lahlali Rachid
Published: August 2023

Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa exhibit comparatively lesser representation. To date, research has largely focused on diseases affecting wheat, sugar beet, potato, maize, and grapevine. Multispectral, reg-green-blue, and hyperspectral sensors were most often used to detect and identify disease symptoms, with current trends pointing to approaches integrating multiple sensors and the use of machine learning and deep learning techniques. Future research should prioritize (i) development of cost-effective and user-friendly UAVs, (ii) integration with emerging agricultural technologies, (iii) improved data acquisition and processing efficiency (iv) diverse testing scenarios, and (v) ethical considerations through proper regulations.
Estimation of Soybean Yield by Combining Maturity Group Information and Unmanned Aerial Vehicle Multi-Sensor Data Using Machine Learning
Authors: Ren Pengting , Li Heli , Han Shaoyu , Chen Riqiang , Yang Guijun , Yang Hao , Feng Haikuan , Zhao Chunjiang
Published: August 2023

Accurate and rapid estimation of the crop yield is essential to precision agriculture. Critical to crop improvement, yield is a primary index for selecting excellent genotypes in crop breeding. Recently developed unmanned aerial vehicle (UAV) platforms and advanced algorithms can provide powerful tools for plant breeders. Genotype category information such as the maturity group information (M) can significantly influence soybean yield estimation using remote sensing data. The objective of this study was to improve soybean yield prediction by combining M with UAV-based multi-sensor data using machine learning methods. We investigated three types of maturity groups (Early, Median and Late) of soybean, and collected the UAV-based hyperspectral and red–green–blue (RGB) images at three key growth stages. Vegetation indices (VI) and texture features (Te) were extracted and combined with M to predict yield using partial least square regression (PLSR), Gaussian process regression (GPR), random forest regression (RFR) and kernel ridge regression (KRR). The results showed that (1) the method of combining M with remote sensing data could significantly improve the estimation performances of soybean yield. (2) The combinations of three variables (VI, Te and M) gave the best estimation accuracy. Meanwhile, the flowering stage was the optimal single time point for yield estimation (R2 = 0.689, RMSE = 408.099 kg/hm2), while using multiple growth stages produced the best estimation performance (R2 = 0.700, RMSE = 400.946 kg/hm2). (3) By comparing the models constructed by different algorithms for different growth stages, it showed that the models built by GPR showed the best performances. Overall, the results of this study provide insights into soybean yield estimation based on UAV remote sensing data and maturity information.
Remotely Sensed Agriculture Drought Indices for Assessing the Impact on Cereal Yield
Authors: Khlif Manel , Escorihuela Maria José , Chahbi Bellakanji Aicha , Paolini Giovanni , Lili Chabaane Zohra
Published: August 2023

This study aims to analyze the potential of different drought indices for identifying drought periods and predicting cereal yield in two semi-arid regions, Lleida in Catalonia and Kairouan in Tunisia, which have similar Mediterranean climates but different agricultural practices. Four drought indices, namely the Soil Moisture Anomaly Index (SMAI), the Vegetation Anomaly Index (VAI), the Evapotranspiration Anomaly Index (EAI), and the Inverse Temperature Anomaly Index (ITAI), were calculated from remote sensing data. Drought periods were identified from 2010/2011 to 2021/2022 based on the aforementioned indices. A correlation study between drought indices and wheat and barley yields was performed in order to select the most informative index and month for yield prediction. In the rainfed cereal area of Lleida, the strongest correlation was found between the EAI and VAI with barley yield (0.91 and 0.83, respectively) at the time of cereal maturity in June. For wheat, the strongest correlation was found between the EAI and VAI (0.75 and 0.72, respectively) at the time of cereal maturity in July. However, the VAI, EAI, and SMAI showed the best performance as an earlier indicator in March with a correlation with barley yield of 0.72, 0.67, and 0.64, respectively; the lowest standard deviation was for the SMAI. For wheat yield, the best earlier indicator was the SMAI in March, showing the highest correlation (0.6) and the lowest standard deviation. For the irrigated cereal zone of Kairouan, the strongest correlation (0.9) and the lowest standard deviation are found between the EAI and cereal yield in April. In terms of advanced prediction, the VAI shows a high correlation in March (0.79) while the SMAI shows a slightly lower correlation in February (0.67) and a lower standard deviation. The results highlight the importance of the EAI and SMAI as key indicators for the estimation and early estimation (respectively) of cereal yield.
An Intelligent Compaction Analyzer: A Versatile Platform for Real-Time Recording, Monitoring, and Analyzing of Road Material Compaction
Authors: Ranasinghe Rajitha , Sounthararajah Arooran , Kodikara Jayantha
Published: August 2023

Intelligent compaction (IC) is a technology that uses non-contact sensors to monitor and record the compaction level of geomaterials in real-time during road construction. However, current IC devices have several limitations: (i) they are unable to visualize or compare multiple intelligent compaction measurement values (ICMVs) in real-time during compaction; (ii) they are not retrofittable to different conventional rollers that exist in the field; (iii) they do not incorporate corrections for ICMVs reflecting variable field conditions; (iv) they are unable to integrate construction specifications as needed for performance-based compaction; and (v) they do not record all the key roller parameters for further compaction analysis. To address these issues, an innovative retrofittable platform with cutting-edge hardware and software was developed. This platform, called the intelligent compaction analyzer (ICA) platform, is effective at calculating conventional acceleration amplitude-based ICMVs and stiffness-based parameters and at displaying the spatial distributions of these parameters in a color-coded map in real-time during compaction.
Design and Realization of Wearable Textile Slotted Waveguide Antennas
Authors: Mikulić Davorin , Šopp Evita , Bonefačić Davor , Bartolić Juraj , Šipuš Zvonimir
Published: August 2023

The design of wearable antennas presents a challenge from multiple perspectives, as they must meet technical requirements and satisfy safety standards while also being suitable for integration into clothing and aesthetically pleasing. In recent years, the development of conductive fabrics has, in many ways, allowed for significant progress in the manufacturing of wearable antennas, and in previous work, we developed textile slotted waveguide antennas using conductive textiles and traditional sewing processes. However, various aspects of the design and realization of such antennas remain challenging. In particular, this work investigates the issue of using foam-based molds, which enables the realization of thin, flexible, wearable antennas, as well as the issue of antenna feed, specifically the transition from a classic coaxial transmission line to a waveguide. The design of the transition was focused on simplicity and robustness, due to which we limited the number of degrees of freedom in the design process in order to achieve a structure suitable for mounting on textile waveguide antennas. In addition, the antenna design procedure and the body-channel model were considered in order to optimize the performance of the antennas and the wireless body-centric system itself. Several prototypes of different kinds were developed in the 5.8 GHz ISM band, confirming the feasibility of the proposed concepts through experimental results.
Rapid IoT Prototyping: A Visual Programming Tool and Hardware Solutions for LoRa-Based Devices
Authors: López Juan José , Lamo Paula
Published: August 2023

LoRa technology has gained popularity as one of the most widely used standards for device interconnection due to its ability to cover long distances and energy efficiency, making it a suitable choice for various Internet of Things (IoT) monitoring and control applications. In this sense, this work presents the development of a visual support tool for creating IoT devices with LoRa and LoRaWAN connectivity. This work significantly advances the state of the art in LoRa technology by introducing a novel visual support tool tailored for creating IoT devices with LoRa and LoRaWAN connectivity. By simplifying the development process and offering compatibility with multiple hardware solutions, this research not only facilitates the integration of LoRaWAN technology within educational settings but also paves the way for rapid prototyping of IoT nodes. The incorporation of block programming for LoRa and LoRaWAN using the Arduinoblocks framework as a graphical environment enhances the capabilities of the tool, positioning it as a comprehensive solution for efficient firmware generation. In addition to the visual tool for firmware generation, multiple compatible hardware solutions enable easy, economical, and stable development, offering a comprehensive hardware and software solution. The hardware proposal is based on an ESP32 microcontroller, known for its power and low cost, in conjunction with an RFM9x module that is based on SX127x LoRa transceivers. Finally, three successfully tested use cases and a discussion are presented.
Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network
Authors: Potărniche Ioana-Adriana , Saroși Codruța , Terebeș Romulus Mircea , Szolga Lorant , Gălătuș Ramona
Published: August 2023

Food additives are utilized in countless food products available for sale. They enhance or obtain a specific flavor, extend the storage time, or obtain a desired texture. This paper presents an automatic classification system for five food additives based on their absorbance in the ultraviolet domain. Solutions with different concentrations were created by dissolving a measured additive mass into distilled water. The analyzed samples were either simple (one additive solution) or mixed (two additive solutions). The substances presented absorbance peaks between 190 nm and 360 nm. Each substance presents a certain number of absorbance peaks at specific wavelengths (e.g., acesulfame potassium presents an absorbance peak at 226 nm, whereas the peak associated with potassium sorbate is at 254 nm). Therefore, each additive has a distinctive spectrum that can be used for classification. The sample classification was performed using deep learning techniques. The samples were associated with numerical labels and divided into three datasets (training, validation, and testing). The best classification results were obtained using CNN (convolutional neural network) models. The classification of the 404 spectra with a CNN model with three convolutional layers obtained a mean testing accuracy of 92.38% ± 1.48%, whereas the mean validation accuracy was 93.43% ± 2.01%.
Research on Internal Shape Anomaly Inspection Technology for Pipeline Girth Welds Based on Alternating Excitation Detection
Authors: Li Rui , Chen Pengchao , Huang Jie , Fu Kuan
Published: August 2023

Abnormal formation of girth weld is a major threat to the safe operation of pipelines, which may lead to serious accidents. Therefore, regular inspection and maintenance of girth weld are essential for accident prevention and energy security. This paper presents a novel method for inspecting abnormal girth weld formation in oil and gas pipelines using alternating excitation detection technology. The method is based on the analysis of the microscopic magnetic variations in the welded area under alternating magnetic fields. An internal inspection probe and electronic system for detecting abnormal girth weld formation were designed and developed. The system’s capability to identify misalignment, undercutting, root concavity, and abnormal formation height of girth weld was tested by numerical simulation and experimental study. The results show that the detection system can effectively identify a minimum misalignment of 0.5 mm at a lift-off height of 15 mm. The proposed method offers several advantages, such as rapid response, low cost, non-contact operation, and high sensitivity to surface flaws in ferromagnetic pipelines.
Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain–Computer Interface Performance
Authors: Siviero Ilaria , Menegaz Gloria , Storti Silvia Francesca
Published: August 2023

(1) Background: in the field of motor-imagery brain–computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.
Fault Detection in 3D Printing: A Study on Sensor Positioning and Vibrational Patterns
Authors: Isiani Alexander , Weiss Leland , Bardaweel Hamzeh , Nguyen Hieu , Crittenden Kelly
Published: August 2023

This work examines the use of accelerometers to identify vibrational patterns that can effectively predict the state of a 3D printer, which could be useful for predictive maintenance. Prototypes using both a simple rectangular shape and a more complex Octopus shape were fabricated and evaluated. Fast Fourier Transform, Spectrogram, and machine learning models, such as Principal Component Analysis and Support Vector Machine, were employed for data analysis. The results indicate that vibrational signals can be used to predict the state of a 3D printer. However, the position of the accelerometers is crucial for vibration-based fault detection. Specifically, the sensor closest to the nozzle could predict the state of the 3D printer faster at a 71% greater sensitivity compared to sensors mounted on the frame and print bed. Therefore, the model presented in this study is appropriate for vibrational fault detection in 3D printers.
Nonlinearities in Fringe-Counting Compact Michelson Interferometers
Authors: Smetana Jiri , Di Fronzo Chiara , Amorosi Anthony , Martynov Denis
Published: August 2023

Compact Michelson interferometers are well positioned to replace existing displacement sensors in the readout of seismometers and suspension systems, such as those used in contemporary gravitational-wave detectors. Here, we continue our previous investigation of a customised compact displacement sensor built by SmarAct that operates on the principle of deep frequency modulation. The focus of this paper is the linearity of this device and its subsequent impact on sensitivity. We show the three primary sources of nonlinearity that arise in the sensor: residual ellipticity, intrinsic distortion of the Lissajous figure, and distortion caused by exceeding the velocity limit imposed by the demodulation algorithm. We verify the theoretical models through an experimental demonstration, where we show the detrimental impact that these nonlinear effects have on device sensitivity. Finally, we simulate the effect that these nonlinearities are likely to have if implemented in the readout of the Advanced LIGO suspensions and show that the noise from nonlinearities should not dominate across the key sub-10 Hz frequency band.
Destriping of Remote Sensing Images by an Optimized Variational Model
Authors: Yan Fei , Wu Siyuan , Zhang Qiong , Liu Yunqing , Sun Haonan
Published: August 2023

Satellite sensors often capture remote sensing images that contain various types of stripe noise. The presence of these stripes significantly reduces the quality of the remote images and severely affects their subsequent applications in other fields. Despite the existence of many stripe noise removal methods in the research, they often result in the loss of fine details during the destriping process, and some methods even generate artifacts. In this paper, we proposed a new unidirectional variational model to remove horizontal stripe noise. The proposed model fully considered the directional characteristics and structural sparsity of the stripe noise, as well as the prior features of the underlying image, to design different sparse constraints, and the ℓp quasinorm was introduced in these constraints to better describe these sparse characteristics, thus achieving a more excellent destriping effect. Moreover, we employed the fast alternating direction method of multipliers (ADMM) to solve the proposed non-convex model. This significantly improved the efficiency and robustness of the proposed method. The qualitative and quantitative results from simulated and real data experiments confirm that our method outperforms existing destriping approaches in terms of stripe noise removal and preservation of image details.
Dark Current Analysis on GeSn p-i-n Photodetectors
Authors: Ghosh Soumava , Sun Greg , Morgan Timothy A. , Forcherio Gregory T. , Cheng Hung-Hsiang , Chang Guo-En
Published: August 2023

Group IV alloys of GeSn have been extensively investigated as a competing material alternative in shortwave-to-mid-infrared photodetectors (PDs). The relatively large defect densities present in GeSn alloys are the major challenge in developing practical devices, owing to the low-temperature growth and lattice mismatch with Si or Ge substrates. In this paper, we comprehensively analyze the impact of defects on the performance of GeSn p-i-n homojunction PDs. We first present our theoretical models to calculate various contributing components of the dark current, including minority carrier diffusion in p- and n-regions, carrier generation–recombination in the active intrinsic region, and the tunneling effect. We then analyze the effect of defect density in the GeSn active region on carrier mobilities, scattering times, and the dark current. A higher defect density increases the dark current, resulting in a reduction in the detectivity of GeSn p-i-n PDs. In addition, at low Sn concentrations, defect-related dark current density is dominant, while the generation dark current becomes dominant at a higher Sn content. These results point to the importance of minimizing defect densities in the GeSn material growth and device processing, particularly for higher Sn compositions necessary to expand the cutoff wavelength to mid- and long-wave infrared regime. Moreover, a comparative study indicates that further improvement of the material quality and optimization of device structure reduces the dark current and thereby increases the detectivity. This study provides more realistic expectations and guidelines for evaluating GeSn p-i-n PDs as a competitor to the III-V- and II-VI-based infrared PDs currently on the commercial market.
A Security Framework for Increasing Data and Device Integrity in Internet of Things Systems
Authors: Dirin Amir , Oliver Ian , Laine Teemu H.
Published: August 2023

The trustworthiness of a system is not just about proving the identity or integrity of the hardware but also extends to the data, control, and management planes of communication between devices and the software they are running. This trust in data and device integrity is desirable for Internet of Things (IoT) systems, especially in critical environments. In this study, we developed a security framework, IoTAttest, for building IoT systems that leverage the Trusted Platform Module 2.0 and remote attestation technologies to enable the establishment of IoT devices’ collected data and control plan traffic integrity. After presenting the features and reference architecture of IoTAttest, we evaluated the privacy preservation and validity through the implementation of two proof-of-concept IoT applications that were designed by two teams of university students based on the reference architecture. After the development, the developers answered open questions regarding their experience and perceptions of the framework’s usability, limitations, scalability, extensibility, potential, and security. The results indicate that IoTAttest can be used to develop IoT systems with effective attestation to achieve device and data integrity. The proof-of-concept solutions’ outcomes illustrate the functionalities and performance of the IoT framework. The feedback from the proof-of-concept developers affirms that they perceived the framework as usable, scalable, extensible, and secure.