bluuebunny/crossref_metadata_2025 huggingface dataset:
Versatility of Saccharomyces cerevisiae 41CM in the Brewery Sector: Use as a Starter for “Ale” and “Lager” Craft Beer Production
Authors: Testa Bruno , Coppola Francesca , Letizia Francesco , Albanese Gianluca , Karaulli Julian , Ruci Mamica , Pistillo Marco , Germinara Giacinto , Messia Maria , Succi Mariantonietta , Vergalito Franca , Tremonte Patrizio , Lombardi Silvia , Iorizzo Massimo
Published: November 2022

Craft breweries tend to use special raw materials and also special ingredients (spices, herbs, fruits) to typify beers, but the metabolic activities of yeasts play a primary role in defining the sensory characteristics of this beverage. Saccharomyces cerevisiae and Saccharomyces pastorianus are yeast species usually used for ale and lager beer production. The selection and use of new yeast starters with peculiar technological and enzymatic characteristics could represent the key point for the production of beers with good and distinctive organoleptic properties. In this study, the fermentative performance of S. cerevisiae 41CM yeast isolated from the vineyard environment for ale and lager craft beer production on a laboratory scale was evaluated. The commercial yeast S. cerevisiae Fermentis S-04 and S. pastorianus Weihenstephan 34/70 were used as reference strains. S. cerevisiae 41CM showed fermentative kinetics similar to commercial starters, both in lager (12 °C) and ale (20 °C) brewing. In all beers brewed, the largest percentage of volatile compounds synthesized during the fermentation were alcohols, followed by esters, terpenes, and aldehydes. In particular, S. cerevisiae 41CM starter contributed a higher relative percentage of esters in the ale beer than that detected in the lager beer, without ever synthesizing unwanted volatile compounds.
UPLC Technique in Pharmacy—An Important Tool of the Modern Analyst
Authors: Gumułka Paweł , Żandarek Joanna , Dąbrowska Monika , Starek Małgorzata
Published: November 2022

In recent years, ultra-efficient liquid chromatography (UPLC) has gained particular popularity due to the possibility of faster separation of small molecules. This technique, used to separate the ingredients present in multi-component mixtures, has found application in many fields, such as chemistry, pharmacy, food, and biochemistry. It is an important tool in both research and production. UPLC created new possibilities for analytical separation without reducing the quality of the obtained results. This technique is therefore a milestone in liquid chromatography. Thanks to the increased resolution, new analytical procedures, in many cases, based on existing methods, are being developed, eliminating the need for re-analysis. Researchers are trying to modify and transfer the analytical conditions from the commonly used HPLC method to UPLC. This topic may be of strategic importance in the analysis of medicinal substances. The information contained in this manuscript indicates the importance of the UPLC technique in drug analysis. The information gathered highlights the importance of selecting the appropriate drug control tools. We focused on drugs commonly used in medicine that belong to various pharmacological groups. Rational prescribing based on clinical pharmacology is essential if the right drug is to be administered to the right patient at the right time. The presented data is to assist the analyst in the field of broadly understood quality control, which is very important, especially for human health and treatment. This manuscript shows that the UPLC technique is now an increasingly used tool for assessing the quality of drugs and determining the identity and content of active substances. It also allows the monitoring of active substances and finished products during their processing and storage.
Quantifying the Ecological Effectiveness of Poverty Alleviation Relocation in Karst Areas
Authors: Feng Qing , Zhou Zhongfa , Zhu Changli , Luo Wanlin , Zhang Lu
Published: November 2022

During the 13th Five-Year Plan period (2015–2020), the Poverty alleviation relocation (PAR), led by the Chinese government in the karst regions of southwest China, aimed to cope with poverty and ameliorate the ecological environment. Nevertheless, few research results have involved quantitative assessment of the ecological effectiveness of PAR. Moreover, few studies on the ecological effects of migration relocation have distinguished the effects of relocation on climatic factors and other ecological restoration projects concerning the ecological environment. It remains unclear to what extent PAR affects the regional ecological environment. In order to quantitatively assess the extent of PAR’s ecological restoration contribution, we adopted the Remote Sensing Ecological Index (RSEI) model, which integrates the four more intuitive and critical influencing factors of greenness, moisture, dryness, and heat. On the Google earth engine (GEE) platform, utilizing its powerful remote sensing data storage capacity and computational capability, we quantitatively assessed the spatial and temporal distribution characteristics of ecological environmental quality (EEQ). As revealed by our research findings, overall EEQ showed a fluctuating upward trend over the period 1996–2021 in the study area, exhibiting an improvement of 22.66%. Mann–Kendall mutation test curves showed the most significant improvement occurred after 2015, with an improvement of 8.06%. Based on the residual analysis model, in order to remove the influence of climatic factors and other anthropogenic activities, and to assess the driving effectiveness of PAR, PAR was remarkedly effective in ameliorating EEQ, causing the RSEI to improve by 0.0221–0.0422. The LISA correlation model further analyzed that 44.91% of regional PAR implementation exerted a remarkable influence on RSEI change, of which 54.59% belonged to positive correlation. Aside from that, we also found that not all areas involved in PAR experienced ameliorated RSEI. In the western region, where the human–land conflict is prominent and the ecology is more fragile, PAR exhibited a significant effect in ameliorating EEQ, but in the eastern region, where the EEQ foundation is better, PAR did not show significant effect, and, thus, the ecological restoration effect of PAR exhibited noticeable geographical suitability.
A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning
Authors: Zhang Huan , Yao Yibin , Hu Mingxian , Xu Chaoqian , Su Xiaoning , Che Defu , Peng Wenjie
Published: November 2022

Global navigation satellite system (GNSS) signals are affected by refraction when traveling through the troposphere, which result in tropospheric delay. Generally, the tropospheric delay is estimated as an unknown parameter in GNSS data processing. With the increasing demand for GNSS real-time applications, high-precision tropospheric delay augmentation information is vital to speed up the convergence of PPP. In this research, we estimate the zenith tropospheric delay (ZTD) from 2018 to 2019 by static precise point positioning (PPP) using the fixed position mode; GNSS observations were obtained from the National Geomatics Center of China (NGCC). Firstly, ZTD outliers were detected, and data gaps were interpolated using the K-nearest neighbor algorithm (KNN). Secondly, The ZTD differences between the KNN and periodic model were employed as input datasets to train the long short-term memory (LSTM) neural network. Finally, LSTM forecasted ZTD differences and the ZTD periodic signals were combined to recover the final forecasted ZTD results. In addition, the forecasted ZTD results were applied in static PPP as a prior constraint to reduce PPP convergence time. Numerical results show that the average root-mean-square error (RMSE) of predicting ZTD is about 1 cm. The convergence time of the PPP which was corrected by the LSTM-ZTD predictions is reduced by 13.9, 22.6, and 30.7% in the summer, autumn, and winter, respectively, over GPT2-ZTD corrected PPP and unconstrained conventional PPP for different seasons.
Estimation of Water Use in Center Pivot Irrigation Using Evapotranspiration Time Series Derived by Landsat: A Study Case in a Southeastern Region of the Brazilian Savanna
Authors: de Sousa Junior Marionei Fomaca , Fonseca Leila Maria Garcia , Bendini Hugo do Nascimento
Published: November 2022

In Brazil, irrigated agriculture is responsible for 46% of withdrawals of water bodies and 67% of use concerning the total water abstracted volume, representing the most significant consumptive use in the country. Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and farming production. In this work, we propose a methodology to estimate water used in agriculture irrigated by center pivots in the municipality of Itobi, São Paulo, in the Brazilian Savanna (known as Cerrado), which has strong potential for agricultural and livestock production. The methodology proposed for the water use estimate is based on mapping crops irrigated by center pivots for the 2015/2016 crop year and actual evapotranspiration (ETa). ETa is derived from the Operational Simplified Surface Energy Balance model (SSEBop) and parameterized for edaphoclimatic conditions in Brazil (SSEBop-Br). Three meteorological data sources (INMET, GLDAS, CFSv2) were tested for estimating ETa. The water use was estimated for each meteorological data source, relating the average irrigation balance and the total area for each crop identified in the map. We evaluated the models for each crop present in the center pivots through global accuracy and f1-score metrics, and f1-score was more significant than 0.9 for all crops. The potato was the crop that consumed the most water in irrigation, followed by soy crops, beans, carrots, and onions, considering the three meteorological data sources. The total water volume consumed by center pivots in the municipality of Itobi in the 2015/2016 agricultural year for each meteorological data source was 3.2 million m3 (INMET), 2.5 million m3; (GLDAS), and 1.8 million m3 (CFSv2).
Snow Depth Inversion in Forest Areas from Sentinel-1 Data Based on Phase Deviation Correction
Authors: Li Yu , Zhao Xinyue , Zhao Quanhua
Published: November 2022

At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This paper presents a snow depth estimation algorithm for forest areas by introducing a forest phase to characterize the effect of forests on backscattering electromagnetic wave. Firstly, the interferogram is generated with the differential interference of two-pass master-slave Synthetic Aperture Radar (SAR) images, and the real phase under snow cover condition is obtained by phase unwrapping. Secondly, the phase models for forest and non-forest areas are constructed. The effects of forest cover are modeled as forest phase in the forest phase model, which is estimated under the assumption of snow depth consistency on both sides of the boundaries between forest and non-forest areas. Finally, snow depth is estimated by the snow phase-depth model. The correctness of the proposed forest snow depth inversion algorithm was verified by taking the Jiagedaqi area of Greater Xing’an Mountains as the study area and sentinel-1 dual polarization images as the data source. Finally, the snow depth distribution of the study area was obtained with a spatial resolution of 30 m on 7 December 2020. The experimental results show that the snow depth values estimated in Jiagedaqi area are mainly between 40–120 cm, and the average snow depth value is 80.27 cm. Taking the snow depth value of 84.69 cm reckoned from hourly accumulated snowfall in Jiagedaqi as the reference snow depth, the results of the estimated snow depth are relatively consistent and well-founded. With the introduction of the forest phase, the average snow depth values estimated in the forest area increase by 5.98 cm, which reduces the underestimation of the snow depth in forest areas.
A 3D Space-Time Non-Local Mean Filter (NLMF) for Land Changes Retrieval with Synthetic Aperture Radar Images
Authors: Pepe Antonio
Published: November 2022

Sequences of multi-temporal synthetic aperture radar (SAR) images are routinely used for land-use land-change (LULC) applications, allowing the retrieval of accurate and up-to-date information on the state of the Earth’s surface and its temporal variations. Change detection (CD) methods that rely on the exploitation of SAR data are, generally, made of three distinctive steps: (1) pre-processing of the SAR images; (2) comparison of the pairs of SAR images; and (3) the automatic extraction of the “changed areas”, employing proper thresholding algorithms. Within this general framework, the reduction in speckle noise effects, which can be obtained by applying spatial multi-looking operations and ad hoc noise filters, is fundamental for the better detecting and classifying of changed regions. Usually, speckle noise filters are singularly and independently applied to every SAR image without the consideration of their inherent temporal relationships. In particular, most use local (spatial) approaches based on determining and averaging SAR backscattered signals related to neighboring SAR pixels. In this work, conversely, we explore the potential of a joint 3D space-time non-local mean filter (NLMF), which relies on the discrimination of similar features in a block of non-local SAR pixels extracted from the same or different SAR images. The theory behind non-local-mean filters is, first, shortly revised. Then, the developed space-time NLMF is applied to a real test case for the purposes of identifying flooded zones due to the massive inundations that hit the Kerala region, India, during the summer of 2018. To this aim, a set of 18 descending SAR images collected from the European (EU) Copernicus Sentinel-1 (S-1) sensor was exploited. The performance of the developed NLMF has also been assessed. It is worth remarking that the proposed method can be applied for the purposes of analyzing a heterogenous set of natural and/or artificial disastrous conditions. Further, it can also be helpful during the pre-processing stages of the sequences of SAR images for the purposes of CD applications.
A VGGNet-Based Method for Refined Bathymetry from Satellite Altimetry to Reduce Errors
Authors: Chen Xiaolun , Luo Xiaowen , Wu Ziyin , Qin Xiaoming , Shang Jihong , Li Bin , Wang Mingwei , Wan Hongyang
Published: November 2022

Only approximately 20% of the global seafloor topography has been finely modeled. The rest either lacks data or its data are not accurate enough to meet practical requirements. On the one hand, the satellite altimeter has the advantages of large-scale and real-time observation. Therefore, it is widely used to measure bathymetry, the core of seafloor topography. However, there is often room to improve its precision. Multibeam sonar bathymetry is more precise but generally limited to a smaller coverage, so it is in a complementary relationship with the satellite-derived bathymetry. To combine the advantages of satellite altimetry-derived and multibeam sonar-derived bathymetry, we apply deep learning to perform multibeam sonar-based bathymetry correction for satellite altimetry bathymetry data. Specifically, we modify a pretrained VGGNet neural network model to train on three sets of bathymetry data from the West Pacific, Southern Ocean, and East Pacific. Experiments show that the correlation of bathymetry data before and after correction can reach a high level, with the performance of R2 being as high as 0.81, and the normalized root-mean-square deviation (NRMSE) improved by over 19% compared with previous research. We then explore the relationship between R2 and water depth and conclude that it varies at different depths. Thus, the terrain specificity is a factor that affects the precision of the correction. Finally, we apply the difference in water depth before and after the correction for evaluation and find that our method can improve by more than 17% compared with previous research. The results show that the VGGNet model can perform better correction to the bathymetry data. Hence, we provide a novel method for accurate modeling of the seafloor topography.
DOA Estimation under GNSS Spoofing Attacks Using a Coprime Array: From a Sparse Reconstruction Viewpoint
Authors: Zhao Yuqing , Shen Feng , Qi Biqing , Meng Zhen
Published: November 2022

The antispoofing method using the direction-of-arrival (DOA) feature can effectively improve the application security of the global navigation satellite system (GNSS) receivers. In this paper, a sparse reconstruction approach based on a coprime array of antennas is proposed to provide reliable DOA estimation under a GNSS spoofing attack. Specifically, the self-coherence property of genuine satellite signals and spoofing was fully exploited to construct a denoised covariance matrix that enables DOA estimation before receiver despreading. Based on this, an equivalent uniform linear array (ULA) was generated from the constructed covariance matrix via virtual array interpolation. By applying the ideal of sparse reconstruction to an equivalent ULA signal, the preliminary DOA estimation results could be obtained without the need for a number of signals. Considering that the sparse estimation technique suffers from basis mismatch effects, we designed an optimization problem with respect to off-grid error to compensate the initial DOA such that the performance loss of DOA estimation could be reduced. Numerical examples demonstrated the advantages of the proposed method in terms of degrees-of-freedom (DOFs), resolution and accuracy.
Vision-Based Detection and Classification of Used Electronic Parts
Authors: Chand Praneel , Lal Sunil
Published: November 2022

Economic and environmental sustainability is becoming increasingly important in today’s world. Electronic waste (e-waste) is on the rise and options to reuse parts should be explored. Hence, this paper presents the development of vision-based methods for the detection and classification of used electronics parts. In particular, the problem of classifying commonly used and relatively expensive electronic project parts such as capacitors, potentiometers, and voltage regulator ICs is investigated. A multiple object workspace scenario with an overhead camera is investigated. A customized object detection algorithm determines regions of interest and extracts data for classification. Three classification methods are explored: (a) shallow neural networks (SNNs), (b) support vector machines (SVMs), and (c) deep learning with convolutional neural networks (CNNs). All three methods utilize 30 × 30-pixel grayscale image inputs. Shallow neural networks achieved the lowest overall accuracy of 85.6%. The SVM implementation produced its best results using a cubic kernel and principal component analysis (PCA) with 20 features. An overall accuracy of 95.2% was achieved with this setting. The deep learning CNN model has three convolution layers, two pooling layers, one fully connected layer, softmax, and a classification layer. The convolution layer filter size was set to four and adjusting the number of filters produced little variation in accuracy. An overall accuracy of 98.1% was achieved with the CNN model.
Analysis of the Security and Reliability of Cryptocurrency Systems Using Knowledge Discovery and Machine Learning Methods
Authors: Shahbazi Zeinab , Byun Yung-Cheol
Published: November 2022

Cryptocurrency, often known as virtual or digital currency, is a safe platform and a key component of the blockchain that has recently attracted much interest. Utilizing blockchain technology, bitcoin transactions are recorded in blocks that provide detailed information on all financial transactions. Artificial intelligence (AI) has significant applicability in several industries because of the abundance and processing capacity of large data. One of the main issues is the absence of explanations for AI algorithms in the current decision-making standards. For instance, there is no deep-learning-based reasoning or control for the system’s input or output processes. More particularly, the bias for adversarial attacks on the process interface and learning characterizes existing AI systems. This study suggests an AI-based trustworthy architecture that uses decentralized blockchain characteristics such as smart contracts and trust oracles. The decentralized consensuses of AI predictors are also decided by this system using AI, enabling secure cryptocurrency transactions, and utilizing the blockchain technology and transactional network analysis. By utilizing AI for a thorough examination of a network, this system’s primary objective is to improve the performance of the bitcoin network in terms of transactions and security. In comparison to other state-of-the-art systems, the results demonstrate that the proposed system can achieve very accurate output.
A Survey of Authentication in Internet of Things-Enabled Healthcare Systems
Authors: Khan Mudassar Ali , Din Ikram Ud , Majali Tha’er , Kim Byung-Seo
Published: November 2022

The Internet of medical things (IoMT) provides an ecosystem in which to connect humans, devices, sensors, and systems and improve healthcare services through modern technologies. The IoMT has been around for quite some time, and many architectures/systems have been proposed to exploit its true potential. Healthcare through the Internet of things (IoT) is envisioned to be efficient, accessible, and secure in all possible ways. Even though the personalized health service through IoT is not limited to time or location, many associated challenges have emerged at an exponential pace. With the rapid shift toward IoT-enabled healthcare systems, there is an extensive need to examine possible threats and propose countermeasures. Authentication is one of the key processes in a system’s security, where an individual, device, or another system is validated for its identity. This survey explores authentication techniques proposed for IoT-enabled healthcare systems. The exploration of the literature is categorized with respect to the technology deployment region, as in cloud, fog, and edge. A taxonomy of attacks, comprehensive analysis, and comparison of existing authentication techniques opens up possible future directions and paves the road ahead.
Wedged Fiber Optic Surface Plasmon Resonance Sensor for High-Sensitivity Refractive Index and Temperature Measurements
Authors: Li Lixia , Li Yuli , Zong Xueyang , Zhao Linlin , Li Penglei , Yu Kun , Liu Yufang
Published: November 2022

Here, we experimentally demonstrate a wedged fiber optic surface plasmon resonance (SPR) sensor enabling high-sensitivity temperature detection. The sensing probe has a geometry with two asymmetrical bevels, with one inclined surface coated with an optically thin film supporting propagating plasmons and the other coated with a reflecting metal film. The angle of incident light can be readily tuned through modifying the beveled angles of the fiber tip, which has a remarkable impact on the refractive index sensitivity of SPR sensors. As a result, we measure a high refractive index sensitivity as large as 8161 nm/RIU in a wide refractive index range of 1.333–1.404 for the optimized sensor. Furthermore, we carry out a temperature-sensitivity measurement by packaging the SPR probe into a capillary filled with n-butanol. This showed a temperature sensitivity reaching up to −3.35 nm/°C in a wide temperature range of 20 °C–100 °C. These experimental results are well in agreement with those obtained from simulations, thus suggesting that our work may be of significance in designing reflective fiber optic SPR sensing probes with modified geometries.
Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
Authors: Mochinski Marcos Alberto , Vieira Marina Luísa de Souza Carrasco , Biczkowski Mauricio , Chueiri Ivan Jorge , Jamhour Edgar , Zambenedetti Voldi Costa , Pellenz Marcelo Eduardo , Enembreck Fabrício
Published: November 2022

In a smart grid communication network, positioning key devices (routers and gateways) is an NP-Hard problem as the number of candidate topologies grows exponentially according to the number of poles and smart meters. The different terrain profiles impose distinct communication losses between a smart meter and a key device position. Additionally, the communication topology must consider the position of previously installed distribution automation devices (DAs) to support the power grid remote operation. We introduce the heuristic method AIDA (AI-driven AMI network planning with DA-based information and a link-specific propagation model) to evaluate the connectivity condition between the meters and key devices. It also uses the link-received power calculated for the edges of a Minimum Spanning Tree to propose a simplified multihop analysis. The AIDA method proposes a balance between complexity and efficiency, eliminating the need for empirical terrain characterization. Using a spanning tree to characterize the connectivity topology between meters and routers, we suggest a heuristic approach capable of alleviating complexity and facilitating scalability. In our research, the interest is in proposing a method for positioning communication devices that presents a good trade-off between network coverage and the number of communication devices. The existing literature explores the theme by presenting different techniques for ideal device placement. Still rare are the references that meticulously explore real large-scale scenarios or the communication feasibility between meters and key devices, considering the detailed topography between the devices. The main contributions of this work include: (1) The presentation of an efficient AMI planning method with a large-scale focus; (2) The use of a propagation model that does not depend on an empirical terrain classification; and (3) The use of a heuristic approach based on a spanning tree, capable of evaluating a smaller number of connections and, even so, proposing a topology that uses fewer router and gateway positions compared to an approach that makes general terrain classification. Experiments in four real large-scale scenarios, totaling over 230,000 smart meters, demonstrate that AIDA can efficiently provide high-quality connectivity demanding a reduced number of devices. Additional experiments comparing AIDA’s detailed terrain-based propagation model to the Erceg-SUI Path Loss model suggest that AIDA can reach the smart meter’s coverage with a fewer router positions.
Detection of Image Level Forgery with Various Constraints Using DFDC Full and Sample Datasets
Authors: Lamichhane Barsha , Thapa Keshav , Yang Sung-Hyun
Published: November 2022

The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard to distinguish from the real images and are being used unethically against famous personalities such as politicians, celebrities, and social workers. Hence, we propose a method to detect these deepfake images using a light weighted convolutional neural network (CNN). Our research is conducted with Deep Fake Detection Challenge (DFDC) full and sample datasets, where we compare the performance of our proposed model with various state-of-the-art pretrained models such as VGG-19, Xception and Inception-ResNet-v2. Furthermore, we perform the experiments with various resolutions maintaining 1:1 and 9:16 aspect ratios, which have not been explored for DFDC datasets by any other groups to date. Thus, the proposed model can flexibly accommodate various resolutions and aspect ratios, without being constrained to a specific resolution or aspect ratio for any type of image classification problem. While most of the reported research is limited to sample or preview DFDC datasets only, we have also attempted the testing on full DFDC datasets and presented the results. Contemplating the fact that the detailed results and resource analysis for various scenarios are provided in this research, the proposed deepfake detection method is anticipated to pave new avenues for deepfake detection research, that engages with DFDC datasets.
Link-State Aware Hybrid Routing in the Terrestrial–Satellite Integrated Network
Authors: Xu Huihui , Shi Zhangsong , Liu Mingliu , Zhang Ning , Yan Yanjun , Han Guangjie
Published: November 2022

In this paper, we study data transmission in the Terrestrial–Satellite Integrated Network (TSIN), where terrestrial networks and satellites are combined together to provide seamless global network services for ground users. However, efficiency of the data transmission is limited by the time-varying inter-satellite link connection and intermittent terrestrial–satellite link connection. Therefore, we propose a link-state aware hybrid routing algorithm, which selects the integrated data transmission path adaptively in this paper. First of all, a space–time topology model is constructed to represent the dynamic link connections in TSIN. Thus, the transmission delay can be analyzed accordingly, and the data transmission problem can then be formulated. To balance the effectiveness and accuracy of searching a hybrid path, we carefully discuss the optimization of space–time topology updating, and propose an inter-satellite link selection algorithm. For the terrestrial–satellite link in hybrid routing, the data transmission problem is transformed into a weighted bipartite graph matching problem and solved with a Kuhn–Munkres-based link selection algorithm. To verify the effectiveness of our proposed routing algorithm, extensive simulations are conducted based on a realistic Hongyun constellation project. Results show that the network performance is improved with respect to data transmission delay, packet loss rate, and throughput.
In Vitro Anti-Colorectal Cancer and Anti-Microbial Effects of Pinus roxburghii and Nauplius graveolens Extracts Modulated by Apoptotic Gene Expression
Authors: Gad Mosab , Hassouna Hassan Z. , Mahmoud Khaled , Abd-Rabou Ahmed A. , Abdel-Azeem Amal S. , Hegazy Amany M. , Abdel-Lattife Mohamed S. , Abdel-Rahim Emam A. , Ahmed Fouad A. , Shim Jae-Han , Zaky Ahmed A.
Published: November 2022

The use of phytochemicals is gaining increasing attention for treating cancer morbidity with minimal burden side effects. This study evaluated the cytotoxicity and antimicrobial activities of Pinus roxburghii branch (P. roxburghii) and Nauplius graveolens (N. graveolens) extracts in vitro. Cell viability was estimated using MTT assay. DNA fragmentation was determined to detect apoptotic pathway initiation. Mechanistically, the apoptotic pathway was tracked by estimating the relative mRNA expression levels of the Bcl-2, Bax, Cas3, NF-κB, and PI3k genes by qRT-PCR. P. roxburghii exhibited moderate antioxidant activity, while N. graveolens possessed highly significant (p < 0.05) scavenging activity against DPPH and ABTS assays. HPLC analysis demonstrated that catechin and chlorogenic acid were the predominant polyphenolic compounds in P. roxburghii and N. graveolens, respectively. The P. roxburghii and N. graveolens extracts inhibited the viability of HCT-116 cells with IC50 values of 30.6 µg mL−1 and 26.5 µg mL−1, respectively. DNA fragmentation analysis showed that the proposed extracts induced apoptosis in HCT-116 cells. Moreover, the IC50 doses of the selected extracts significantly (p < 0.05) upregulated Bax and cleaved Cas-3, and downregulated Bcl-2, NF-κB, and PI3k genes versus the GAPDH gene as a housekeeping gene in comparison to the control group. The Bax/Bcl-2 ratio was raised upon treatment. The mentioned extracts exhibited antimicrobial action against all tested bacteria and fungi. The highest antibacterial effect was recorded against E. coli, with inhibition zones of 12.0 and 11.2 mm for P. roxburghii and N. graveolens, respectively. On the other hand, the highest antifungal action was registered for Penicillium verrucosum and A. niger, with inhibition zones of 9.8 and 9.2 mm for the tested extracts, respectively. In conclusion, the outcomes of this study indicate that P. roxburghii and N. graveolens extracts could potentially be used as anticancer, antibacterial, and antifungal agents.
Conservation Agriculture as a Sustainable System for Soil Health: A Review
Authors: Cárceles Rodríguez Belén , Durán-Zuazo Víctor Hugo , Soriano Rodríguez Miguel , García-Tejero Iván F. , Gálvez Ruiz Baltasar , Cuadros Tavira Simón
Published: November 2022

Soil health is a term used to describe the general state or quality of soil, and in an agroecosystem, soil health can be defined as the ability of the soil to respond to agricultural practices in a way that sustainably supports both agricultural production and the provision of other ecosystem services. Conventional agricultural practices cause deterioration in soil quality, increasing its compaction, water erosion, and salinization and decreasing soil organic matter, nutrient content, and soil biodiversity, which negatively influences the productivity and long-term sustainability of the soil. Currently, there are many evidences throughout the world that demonstrate the capability of conservation agriculture (CA) as a sustainable system to overcome these adverse effects on soil health, to avoid soil degradation and to ensure food security. CA has multiple beneficial effects on the physical, chemical, and biological properties of soil. In addition, CA can reduce the negative impacts of conventional agricultural practices on soil health while conserving the production and provision of soil ecosystem services. Today, agricultural development is facing unprecedented challenges, and CA plays a significant role in the sustainability of intensive agriculture. This review will discuss the impact of conservation agricultural practices on soil health and their role in agricultural sustainability.
Special Issue “Talent Identification and Development in Youth Sports”
Authors: Kelly Adam , Calvo Alberto , dos Santos Sara , Jiménez Sáiz Sergio
Published: November 2022

We are delighted to share our Special Issue on Talent Identification and Development in Youth Sports [...]
On the Relation between Lambert W-Function and Generalized Hypergeometric Functions
Authors: Rathie Pushpa Narayan , Ozelim Luan Carlos de Sena Monteiro
Published: November 2022

In the theory of special functions, finding correlations between different types of functions is of great interest as unifying results, especially when considering issues such as analytic continuation. In the present paper, the relation between Lambert W-function and generalized hypergeometric functions is discussed. It will be shown that it is possible to link these functions by following two different strategies, namely, by means of the direct and inverse Mellin transform of Lambert W-function and by solving the trinomial equation originally studied by Lambert and Euler. The new results can be used both to numerically evaluate Lambert W-function and to study its analytic structure.
Analyzing the Effects of Urban Photopollution on Photosynthetic Efficiency of Certain Trees through Chlorophyll Fluorescence OJIP Transient
Authors: Kumar Deepak , Singh Hanwant , Bhatt Upma , Soni Vineet
Published: November 2022

Trees in urban areas provide important ecosystem services and are an essential element of urban green space. The constant increase in artificial light from anthropogenic activities around the world creates photopollution that affects the phenology and physiology of plants. Here we conducted a field study to investigate the anthropogenic impacts on six urban trees (Saraca asoca, Terminalia catappa, Bauhinia variegata, Holoptelea integrifolia, Ficus benjamina and Thevetia peruviana) using chlorophyll fluorescence analysis. OJIP curve, maximum quantum yield of primary photochemistry (ΦPo), quantum yield of electron transport (ΦEo), probability that an absorbed photon will be dissipated (ΦDo), photosynthetic performance index (PIcsm) and reaction center photochemistry were assessed. According to the results, various parameters of chlorophyll fluorescence showed significant and important effects on different tree species. T. peruviana and F. benjamina were found to be tolerant to street lighting, while on the other hand, S. asoca, T. catappa, B. variegata and H. integrifolia were found to be sensitive to artificial light induced by street lamps. This study clearly indicates that chlorophyll fluorescence analysis is a potent method for screening the tolerance of tree species to photopollution induced by artificial lights.
Comprehensive Assessment of Soil Chemical Properties for Land Reclamation Purposes in the Toshka Area, EGYPT
Authors: Al-Soghir Mostafa M. A. , Mohamed Ahmed G. , El-Desoky Mohamed A. , Awad Ahmed A. M.
Published: November 2022

Among soil parameters, Soil Chemical Properties (SCPs) are a crucial factor for the evaluation of chemical and fertility indices, proper decision-making regarding land reclamation, and land cultivation. In this work, 32 samples were collected from the surface (0–30 cm) and subsurface (30–60 cm) layers from 16 selected positions using GIS in the Toshka area (23°30′ and 23°60′ N latitude and 31°32′ and 31°36′ E longitude), Aswan, Egypt. Our results revealed that the soil reaction ranged from slightly (7.25) to moderately alkaline (8.19) and was characterized as a moderately saline and calcareous soil; however, the minimum ECe and CaCO3 values exceeded 4.30 dS·m−1 and 12.80%, respectively. Furthermore, there was a significant decrease in the soil organic matter (not exceeding 0.11%) and available nutrient contents. The cation exchange capacity was moderate. The correlation coefficient between the studied SCPs fluctuated between positive and negative. In brief, the area can be reclaimed through a proper reclaiming plan and the selection of the most suitable crops to treat the undesirable characteristics.
Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review
Authors: Yue Miao , Jong Morris Siu-Yung , Dai Yun
Published: November 2022

In response to the growing popularity of artificial intelligence (AI) usage in daily life, AI education is increasingly being provided at the K-12 level, with relevant initiatives being launched worldwide. Examining how these programs have been implemented and summarizing useful experiences is thus imperative. Although prior reviews have described the characteristics of AI education programs in publications, the papers reviewed were mostly nonempirical reports, and the analysis typically only involved a descriptive summary. The current review focuses on the most recent empirical studies on AI teaching programs in K-12 contexts through a systematic search of the Web of Science database from 2010 to 2022. To provide a comprehensive overview of the status of AI teaching and learning (T&L), 32 empirical studies were analyzed both descriptively and thematically. We analyzed (1) the research status, (2) the pedagogical design, and (3) the assessments and outcomes of the AI teaching programs. An increasing number of studies have focused on AI education at the K-12 stage, but most of them have a small sample size. Moreover, the data were mostly collected through interviews and self-reports. We reviewed the pedagogical design of AI teaching programs by using Gerlach and Ely’s pedagogical design model. The results comprehensively delineated current AI teaching programs through nine dimensions: learning theory, pedagogical approach, T&L activities, learning content, scale, teaching resources, prior knowledge prerequisite, aims and objectives, assessment, and learning outcome. The results highlighted the positive impact of current AI teaching programs on students’ motivation, engagement, and attitude. However, we observed a lack of sufficient research objectively measuring students’ knowledge acquisition as learning outcomes. Overall, in this paper, we discussed relevant findings in terms of research trends, learning content, teaching units, characteristics of the pedagogical design, and assessment and evaluation by providing illustrations of exemplary designs; we also discussed future directions for research and practice in AI education in the K-12 context.
Performance Evaluations and Influencing Factors of the Agricultural Product Trade Supply Chain between China and Central Asian Countries
Authors: Abula Kahaer , Abula Buwajian , Wang Xinyu , Wang Dezhen
Published: November 2022

With the development of global economic integration and the continuous promotion of the “Belt and Road” initiative, the agricultural trade supply chain has become an important part of the agricultural trade supply chain of countries along the “Belt and Road”. Through transnational connectivity, the level of economic and trade cooperation has been improved, providing favorable conditions for the development of a high-quality trade supply chain serving broad areas. Therefore, building a sound and stable supply chain of agricultural products has important practical and theoretical significance for improving the level of economic and trade cooperation of countries along the “Belt and Road” and promoting China’s high-quality economic development. The results show that the trend line of agricultural product trade supply chain performance between China and Central Asia shows a significant downward trend (non-Data Envelopment Analysis efficiency still accounts for a large proportion, and the comprehensive benefits are affected by economies of scale, which is mainly related to the economic conditions of Central Asian countries). The influencing factors of each dimension have significant positive effects on the performance of the agricultural trade supply chain between China and Central Asia to varying degrees and can maximize the performance of China’s Central Asian agricultural trade supply chain through the influencing factors of each dimension of Central Asian countries within the optimal promotion interval. This study recommends improving the information processing efficiency of both parties, predicting market demand, and shortening relevant payment and trade links to improve the efficiency of logistics and transportation between China and Central Asian countries. China’s assistance to the logistics and transportation infrastructure of Central Asian countries can effectively strengthen the connectivity between China and Central Asian countries to promote the overall performance level of the supply chain and achieve mutually beneficial results.
Human-in-Loop Decision-Making and Autonomy: Lessons Learnt from the Aviation Industry Transferred to Cyber-Physical Systems
Authors: Makri Chara , Gürdür Broo Didem , Neely Andy
Published: November 2022

In this study, we reviewed aircraft accidents in order to understand how autonomy and safety has been managed in the aviation industry, with the aim of transferring our findings to autonomous cyber-physical systems (CPSs) in general. Through the qualitative analysis of 26 reports of aircraft accidents that took place from 2016 to 2022, we identified the most common contributing factors and the actors involved in aircraft accidents. We found that accidents were rarely the result of a single event or actor, with the most common contributing factor being non-adherence to standard operating procedures (SOPs). Considering that the aviation industry has had decades to perfect their SOPs, it is important for CPSs not only to consider the actors and causes that may contribute to safety-related issues, but also to consider well-defined reporting practices, as well as the different levels of mechanisms checked by diverse stakeholders, in order to minimise the cascading nature of such events to improve safety. In addition to proposing a new definition of safety, in this study we suggest reviewing high-reliability organisations to offer further insights as part of future research on CPS safety.
Evaluation of Toxic Effects Induced by Sub-Acute Exposure to Low Doses of α-Cypermethrin in Adult Male Rats
Authors: Kašuba Vilena , Tariba Lovaković Blanka , Lucić Vrdoljak Ana , Katić Anja , Kopjar Nevenka , Micek Vedran , Milić Mirta , Pizent Alica , Želježić Davor , Žunec Suzana
Published: November 2022

To contribute new information to the pyrethroid pesticide α-cypermethrin toxicity profile, we evaluated its effects after oral administration to Wistar rats at daily doses of 2.186, 0.015, 0.157, and 0.786 mg/kg bw for 28 days. Evaluations were performed using markers of oxidative stress, cholinesterase (ChE) activities, and levels of primary DNA damage in plasma/whole blood and liver, kidney, and brain tissue. Consecutive exposure to α-cypermethrin affected the kidney, liver, and brain weight of rats. A significant increase in concentration of the thiobarbituric acid reactive species was observed in the brain, accompanied by a significant increase in glutathione peroxidase (GPx) activity. An increase in GPx activity was also observed in the liver of all α-cypermethrin-treated groups, while GPx activity in the blood was significantly lower than in controls. A decrease in ChE activities was observed in the kidney and liver. Treatment with α-cypermethrin induced DNA damage in the studied cell types at almost all of the applied doses, indicating the highest susceptibility in the brain. The present study showed that, even at very low doses, exposure to α-cypermethrin exerts genotoxic effects and sets in motion the antioxidative mechanisms of cell defense, indicating the potential hazards posed by this insecticide.
Alpha Lipoic Acid and Monoisoamyl-DMSA Combined Treatment Ameliorates Copper-Induced Neurobehavioral Deficits, Oxidative Stress, and Inflammation
Authors: Patwa Jayant , Thakur Ashima , Flora Swaran Jeet Singh
Published: November 2022

Copper (Cu), being an essential trace metal, plays several roles in biological processes, though exposure to Cu can be potentially toxic to the brain and a few other soft organs. In the present study, we investigated the effects of the combined administration of monoisoamyl 2, 3-dimercaptosuccinic acid (MiADMSA), which is a new chelator, and alpha lipoic acid (ALA) and an antioxidant that is made naturally in the body and is also found in foods, against Cu-induced oxidative stress in rats. Rats were exposed to 20 mg/kg copper sulfate for 16 weeks once a day via the oral route. After 16 weeks of exposure, animals were divided into different sub-groups. Group I was divided into three subgroups: Group IA, control; Group IB, MiADMSA (75 mg/kg, oral); Group IC, ALA (75 mg/kg, oral), while Group II was divided into four subgroups: Group IIA, Cu pre-exposed; Group IIB, Cu+ MiADMSA; Group IIC, Cu+ ALA; Group IID, Cu+ ALA+ MiADMSA. Exposure to Cu led to significant neurobehavioral abnormalities; treatment with MiADMSA, and in particular MiADMSA + ALA, significantly ameliorated the neurobehavioral parameters and restored the memory deficits in rats. Oxidative stress variables (ROS, nitrite, TBARS, SOD, catalase) and inflammatory markers (TNF-α, and IL-1β), which were altered on Cu exposed rats, also responded favorably to ALA+ MiADMSA combined treatment. Thus, combined administration of MiADMSA and ALA might be a better treatment strategy than monotherapy with MiADMSA or ALA against Cu-induced neurotoxicity, particularly in reducing oxidative stress, neurobehavioral abnormalities, and inflammatory markers.