bluuebunny/crossref_metadata_2025 huggingface dataset:
The MR Imaging of Primary Intrahepatic Lymphoepithelioma-like Cholangiocarcinoma: A Diagnostic Challenge
Authors: Liu Yangyang , Guo Dajing , He Xiaojing , Liu Xi , Chen Weijie , Chen Lingli , Ji Yuan , Zeng Mengsu , Wang Mingliang
Published: September 2023

Purpose: To characterize the magnetic resonance imaging features of primary intrahepatic lymphoepithelioma-like cholangiocarcinoma (LELCC). Materials and Methods: Thirty-four patients with 38 histologically confirmed LELCCs were enrolled retrospectively from January 2014 to August 2022. We evaluated the clinical features, histologic findings, and imaging manifestations on dynamic enhanced MRI. Results: 74% (25/34) of the cases were associated with EBV infection. Moreover, patients infected with EBV exhibited a lower level of Ki-67 proliferation. The serum CA199 level was elevated in 10 patients. The median tumor diameter was 2.8 cm (range, 1.1–8.7 cm). Most tumors were well-defined with a smooth or lobulated margin and showed peripheral hyperintensity and central hypointensity on T2-weighted imaging (T2WI). T2 hyperintense foci were recognized in 8 patients. In the dynamic enhanced MRI, 21 tumors demonstrated Type A enhancement pattern (rim enhancement), 10 demonstrated Type B (rapid wash-in and wash-out), and seven demonstrated Type C (rapid wash-in without wash-out). Capsular enhancement in PVP or DP was found in 22 tumors. A few patients had satellite lesions, portal vein thrombosis, bile duct dilatation, and distal metastasis. Lymph node metastases were discovered pathologically in 11 patients. Conclusions: MRI findings of LELCC vary and are non-specific. While a majority of LELCCs exhibit typical features of intrahepatic cholangiocarcinoma (iCCA), unique findings like T2 hyperintense foci or capsular enhancement could suggest LELCC. EBV infection and elevated tumor markers can aid in differentiation. However, given the mimics of some cases of liver hypervascular lesions, histological examination remains essential for definitive diagnosis.
The Usefulness of the Ratio of Antigen–Autoantibody Immune Complexes to Their Free Antigens in the Diagnosis of Non-Small Cell Lung Cancer
Authors: Kim Heyjin , Lee Jin Kyung , Oh Ae-Chin , Kim Hye-Ryoun , Hong Young Jun
Published: September 2023

Autoantibodies against specific lung cancer-associated antigens have been suggested for the performance of lung cancer diagnosis. This study aimed to evaluate the diagnostic performance of the antigen–autoantibody immune complex (AIC) against its free antigens for CYFRA21-1, ProGRP, neutrophil gelatinase-associated lipocalin (NGAL), and neuron-specific enolase (NSE) in non-small cell lung cancer (NSCLC). In total, 85 patients with NSCLC and 120 healthy controls (HCs) were examined using a 9-guanine DNA chip method. The ratios of AICs to their antigens and the combinations of ratios consisting of two to four markers were calculated. The levels of AICs for CYFRA21-1, ProGRP, NGAL, and NSE were higher than those for their free antigens in all participants. The levels of each free antigens distinguished patients with NSCLC from the HCs. The ratios of the AIC to its antigen and seven combinations of two to four ratios were significantly higher in patients with NSCLC than in the HCs. Excellent diagnostic performance was observed for all combination ratios (C4-1), with 85.9% sensitivity and 86.7% specificity at a 3.51 cut-off. Higher sensitivity was observed in the early stages (0–I) and adenocarcinoma than in stages II–IV and other pathological types. Combining all ratios of AICs and their antigens for all four markers was useful when diagnosing NSCLC.
Joint Resource Allocation and Drones Relay Selection for Large-Scale D2D Communication Underlaying Hybrid VLC/RF IoT Systems
Authors: Liu Xuewen , Huang Shuman , Zhang Kaisa , Maimaiti Saidiwaerdi , Chuai Gang , Gao Weidong , Chen Xiangyu , Hou Yijian , Zuo Peiliang
Published: September 2023

Relay-aided Device-to-Device (D2D) communication combining visible light communication (VLC) with radio frequency (RF) is a promising paradigm in the internet of things (IoT). Static relay limits the flexibility and maintaining connectivity of relays in Hybrid VLC/RF IoT systems. By using a drone as a relay station, it is possible to avoid obstacles such as buildings and to communicate in a line-of-sight (LoS) environment, which naturally aligns with the requirement of VLC Systems. To further support the application of VLC in the IoT, subject to the challenges imposed by the constrained coverage, the lack of flexibility, poor reliability, and connectivity, drone relay-aided D2D communication appears on the horizon and can be cost-effectively deployed for the large-scale IoT. This paper proposes a joint resource allocation and drones relay selection scheme, aiming to maximize the D2D system sum rate while ensuring the quality of service (QoS) requirements for cellular users (CUs) and D2D users (DUs). First, we construct a two-phase coalitional game to tackle the resource allocation problem, which exploits the combination of VLC and RF, as well as incorporates a greedy strategy. After that, a distributed cooperative multi-agent reinforcement learning (MARL) algorithm, called WoLF policy hill-climbing (WoLF-PHC), is proposed to address the drones relay selection problem. Moreover, to further reduce the computational complexity, we propose a lightweight neighbor–agent-based WoLF-PHC algorithm, which only utilizes historical information of neighboring DUs. Finally, we provide an in-depth theoretical analysis of the proposed schemes in terms of complexity and signaling overhead. Simulation results illustrate that the proposed schemes can effectively improve the system performance in terms of the sum rate and outage probability with respect to other outstanding algorithms.
Degradation-Sensitive Health Indicator Construction for Precise Insulation Degradation Monitoring of Electromagnetic Coils
Authors: Sun Yue , Wang Kai , Xu Aidong , Guan Beiye , Li Ruiqi , Zhang Bo , Zhou Xiufang
Published: September 2023

Electromagnetic coils are indispensable components for energy conversion and transformation in various systems across industries. However, electromagnetic coil insulation failure occurs frequently, which can lead to serious consequences. To facilitate predictive maintenance for industrial systems, it is essential to monitor insulation degradation prior to the formation of turn-to-turn shorts. This paper experimentally investigates coil insulation degradation from both macro and micro perspectives. At the macro level, an evaluation index based on a weighted linear combination of trend, monotonicity and robustness is proposed to construct a degradation-sensitive health indicator (DSHI) based on high-frequency electrical response parameters for precise insulation degradation monitoring. While at the micro level, a coil finite element analysis and twisted pair accelerated degradation test are conducted to obtain the actual turn-to-turn insulation status. The correlation analysis between macroscopic and microscopic effects of insulation degradation is used to verify the proposed DSHI-based method. Further, it helps to determine the threshold of DSHI. This breakthrough opens new possibilities for predictive maintenance for industrial equipment that incorporates coils.
Quantum and Quantum-Inspired Stereographic K Nearest-Neighbour Clustering
Authors: Viladomat Jasso Alonso , Modi Ark , Ferrara Roberto , Deppe Christian , Nötzel Janis , Fung Fred , Schädler Maximilian
Published: September 2023

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to not currently provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed ‘quantum-inspired’ algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.
Underrepresented Students in Gifted and Talented Education: Using Positive Psychology to Identify and Serve
Authors: Arnstein Karen B. , Desmet Ophélie Allyssa , Seward Kristen , Traynor Anne , Olenchak F. Richard
Published: September 2023

The representation gap in gifted and talented education poses a persistent challenge in educational systems worldwide. This theoretical manuscript presents the Bull’s Eye Model for Affective Development—Expansion (BEM-e) an innovative framework designed to address this gap. By incorporating elements from positive psychology, the BEM-e aims to identify and nurture traditionally underrepresented students who possess camouflaged gifted and talented abilities. Drawing upon the Engagement, Perseverance, Optimism, Connectedness, and Happiness model (EPOCH), along with measures of hope and metacognition, BEM-e provides a comprehensive approach to talent identification and service. The model emphasizes the holistic development of individuals by considering affective factors, engagement, perseverance, optimism, connectedness, happiness, hope, and metacognition. Additionally, dynamic assessment is integrated during the implementation of BEM-e modules, allowing for personalized and adaptive identification processes.
Addressing Language and Study Skills Challenges in Online Undergraduate EMI Courses
Authors: Kohnke Lucas , Jarvis Andrew
Published: September 2023

English-medium instruction (EMI) is taking hold within higher education in non-Anglophone settings, but there is insufficient research into the challenges students encounter when taking EMI courses online. This exploratory quantitative study conducted in Hong Kong examines the language and studying challenges faced by undergraduate students when in-person classes were suspended due to COVID-19. One hundred thirteen first- and second-year students completed a questionnaire, rating their perceived challenges in the areas of writing, speaking, reading, listening, and study skills. The results showed that they faced particular challenges with reading and study skills (especially self-motivation), as well as vocabulary range, which affected more than one skill. Corroborating existing research, students with less secondary school EMI experience reported greater challenges. As providing English for Academic Purposes (EAP) courses is a primary way to support the language skills of students in EMI settings, we offer guidance to EAP practitioners who seek to help their students overcome the challenges identified in this article. As online technology continues to deliver content in tertiary education, EAP courses must be closely aligned with the language and study skills needs of students in digital EMI environments.
Impact of an ML-Based Demand Response Mechanism on the Electrical Distribution Network: A Case Study in Terni
Authors: Bucarelli Marco Antonio , Ghoreishi Mohammad , Santori Francesca , Mira Jorge , Gorroñogoitia Jesús
Published: September 2023

The development of smart grids requires the active participation of end users through demand response mechanisms to provide technical benefits to the distribution network and receive economic savings. Integrating advanced machine learning tools makes it possible to optimise the network and manage the mechanism to maximise the benefits. This paper proceeds by forecasting consumption for the next 24 h using a recurrent neural network and by processing these data using a reinforcement learning-based optimisation model to identify the best demand response policy. The model is tested in a real environment: a portion of the Terni electrical distribution network. Several scenarios were identified, considering users’ participation at different levels and limiting the potential with various constraints.
Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation
Authors: Rzayeva Leila , Myrzatay Ali , Abitova Gulnara , Sarinova Assiya , Kulniyazova Korlan , Saoud Bilal , Shayea Ibraheem
Published: September 2023

Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.
RETRACTED: A Global Structural Hypergraph Convolutional Model for Bundle Recommendation
Authors: Liu Xingtong , Yuan Man
Published: September 2023

Bundle recommendations provide personalized suggestions to users by combining related items into bundles, aiming to enhance users’ shopping experiences and boost merchants’ sales revenue. Existing solutions based on graph neural networks (GNN) face several significant challenges: (1) it is demanding to explicitly model multiple complex associations using standard graph neural networks, (2) numerous additional nodes and edges are introduced to approximate higher-order associations, and (3) the user–bundle historical interaction data are highly sparse. In this work, we propose a global structural hypergraph convolutional model for bundle recommendation (SHCBR) to address the above problems. Specifically, we jointly incorporate multiple complex interactions between users, items, and bundles into a relational hypergraph without introducing additional nodes and edges. The hypergraph structure inherently incorporates higher-order associations, thereby alleviating the training burden on neural networks and the dilemma of scarce data effectively. In addition, we design a special matrix propagation rule that captures non-pairwise complex relationships between entities. Using item nodes as links, structural hypergraph convolutional networks learn representations of users and bundles on a relational hypergraph. Experiments conducted on two real-world datasets demonstrate that the SHCBR outperforms the state-of-the-art baselines by 11.07–25.66% on Recall and 16.81–33.53% on NDCG. Experimental results further indicate that the approach based on hypergraphs can offer new insights for addressing bundle recommendation challenges. The codes and datasets have been publicly released on GitHub.
A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
Authors: Luo Weilin , Jin Hongbin , Li Xiaobai , Li Hao , Liu Kang , Yang Ruijuan
Published: September 2023

The separation of time–frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow convergence speed, and poor robustness in low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR) scenarios. To address the above issues, this paper firstly establishes a time delay mixing mathematical model. A robust whitening algorithm is proposed by using the time delay correlation matrix of the observed signal, which is insensitive to noise. Secondly, the joint diagonalized F-parametrization is used as the objective function, and the separation matrix is constructed based on the multiple complex-valued Givens matrices. The complex-valued Givens matrix not only ensures orthogonality in the separation matrix but also effectively reduces the number of parameters to be calculated. This approach guarantees accuracy and simplifies the complexity of the separation process. Finally, the nonlinear chaotic grey wolf optimizer is utilized to search for the optimal rotation angle. The simulation results demonstrate that this algorithm offers higher separation accuracy and requires fewer iterations compared to the traditional algorithm. Additionally, it enhances the accuracy of direction of arrival (DOA) estimation, reduces the communication bit error rate, and enables the joint estimation of the target distance and velocity even in the presence of powerful jamming and a low SNR.
Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture
Authors: Shin Hyeju , Oh Seungmin , Isah Abubakar , Aliyu Ibrahim , Park Jaehyung , Kim Jinsul
Published: September 2023

The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital twin network and network modeling from a data perspective. In addition, a network traffic modeling technique using data feature extraction techniques is proposed to realize the digital twin network, which requires the use of massive streaming data. The proposed method utilizes the data generated in the OMNeT++ environment and verifies that the learning time is reduced by approximately 25% depending on the feature extraction interval, while the accuracy remains similar.
Cybersecurity Risk Analysis in the IoT: A Systematic Review
Authors: AlSalem Thanaa , Almaiah Mohammed , Lutfi Abdalwali
Published: September 2023

The Internet of Things (IoT) is increasingly becoming a part of our daily lives, raising significant concerns about future cybersecurity risks and the need for reliable solutions. This study conducts a comprehensive systematic literature review to examine the various challenges and attacks threatening IoT cybersecurity, as well as the proposed frameworks and solutions. Furthermore, it explores emerging trends and identifies existing gaps in this domain. The study’s novelty lies in its extensive exploration of machine learning techniques for detecting and countering IoT threats. It also contributes by highlighting research gaps in economic impact assessment and industrial IoT security. The systematic review analyzes 40 articles, providing valuable insights and guiding future research directions. Results show that privacy issues and cybercrimes are the primary concerns in IoT security, and artificial intelligence holds promise for future cybersecurity. However, some attacks remain inadequately addressed by existing solutions, such as confidentiality, security authentication, and data server connection attacks, necessitating further research and real-life testing of proposed remedies.
Soil Quality Evaluation of Typical Vegetation and Their Response to Precipitation in Loess Hilly and Gully Areas
Authors: Xiang Ting , Qiang Fangfang , Liu Guangquan , Liu Changhai , Liu Yingfei , Ai Ning , Ma Huan
Published: September 2023

The selection of suitable tree species and the reasonable allocation of planting areas are important measures for improving soil quality. This study aimed to investigate the characteristics of typical vegetation type soil quality differences and their dominant factors in loess hilly–gully areas after returning farmland to the forest (grassland). The soil quality status and dominant factors of arbors, shrubs and grasslands in the study area were comprehensively analyzed using the soil quality index (SQI) and structural equation modeling (SEM). The results showed the following: (1) In the study area, the shrub forest had a high capacity for air permeability, water retention and nitrogen fixation. (2) The soil quality of the three vegetation types improved with increasing precipitation, and the soil quality indicator of shrubs was the highest, indicating a better soil quality improvement. However, the soil quality of the arbors and grasslands showed a greater percentage increase. In the precipitation range of 400–410 mm, the soil quality of shrub forests was significantly higher than that of arbors and grasslands. (3) Structural equation modeling analysis indicated that precipitation, vegetation and soil factors are closely related to soil quality. Further analysis showed that soil bulk density, porosity, capillary water-holding capacity, soil organic carbon and total phosphorus were the dominant factors affecting the soil quality in the study area. The purpose of this study was to evaluate quantitatively the soil quality after different vegetation types under different precipitation gradients, to clarify the variation trend of soil quality at different vegetation types with different precipitation gradients and to provide a scientific basis and data support for the quantitative evaluation of vegetation restoration and selection of tree species and vegetation configuration within different precipitation gradients in loess hilly and gully regions in the future.
Trends in Brazil’s Forestry Education—Part 3: Employment Patterns of Forest Engineering Graduates from Two Public Universities in the Last 15 Years
Authors: Hakamada Rodrigo , Frosini de Barros Ferraz Silvio , Sulbarán-Rangel Belkis , Lucena Luiza , Palacios Hinestroza Hasbleidy
Published: September 2023

The employment rate of graduates and the interest of organizations in a high-skilled professional workforce are essential drivers of actions within the universities to improve the qualifications of college students. Our objective was to identify the sectors of employment pursued by graduates from forestry engineering programs of the Luiz de Queiroz College of Agriculture (ESALQ/USP) and of the Federal Rural University of Pernambuco (UFRPE). We also conducted a survey among companies affiliated with the Forest Science and Research Institute (IPEF) to understand the percentage of forest engineers employed by these organizations to gain insights into the scale of this occupation in Brazil. We established two graduate cohorts to investigate the impact of changes in the last five years. In the first cohort (2008–2017), 82% and 40% of graduates from ESALQ/USP and UFRPE were involved in the forestry sector, respectively. However, in the second cohort (2018–2022), the percentage increased from 82% to 97% for graduates from ESALQ/USP, and after changes in the program, the percentage of graduates from UFRPE engaged in the forestry industry significantly rose from 40% to 73%. We discussed the reasons for these increases, mainly in terms of the growing market demand for forestry professionals. In the case of UFRPE, the boost in employment was attributed to the creation of a group for forestry practices along with actions to approach the private sector. We also found a strong ratio of one forest engineer to 4000 ha of planted forest among the enterprises associated with IPEF, indicating a substantial potential of employment in the forestry sector.
Modification of Density Dependence and Habitat Filtering on Seedling Survival of Different Mycorrhizal-Type Tree Species in Temperate Forests
Authors: Li Jian , Zhao Xiuhai
Published: September 2023

Conspecific negative density dependence (CNDD) and habitat filtering are critical to seedling survival. However, the relative importance of the two processes in affecting survival of seedlings with different types of mycorrhizae remains unclear. In this study, the effects of CNDD and habitat filtering on the survival of tree seedlings with different mycorrhizal types were investigated at different successional stages of a temperate forest in the Changbai Mountain Natural Reserve, Northeast China. Conspecific negative density dependence and habitat filtering significantly affected seedling survival. In the early successional stage, the interactions between conspecific neighbor tree density and light availability and soil properties significantly negatively affected survival of all species and arbuscular mycorrhizal (AM) seedlings in the community, but not that of ectomycorrhizal (EcM) seedlings, and the CNDD effect was stronger on AM seedlings than on EcM seedlings. In the mid-successional stage, CNDD effects were stronger on EcM seedlings. Therefore, different types of mycorrhizal seedlings responded differently to CNDD and habitat filtering mechanisms during community succession, and thus, tree mycorrhizal association could determine the effects of CNDD and habitat filtering on seedling survival in temperate forests.
Biological Detoxification of the Inhibitors in Corncob Acid Hydrolysate Using Aspergillus niger
Authors: Yin Jinbao , Wang Chen , Li Yilian , Lv Bo , Lv Haosheng , Xue Yuyuan , Wu Jing , Zhang Jianan
Published: September 2023

The biological detoxification of lignocellulose hydrolysate is an effective method through which to enhance microbial fermentation efficiency. In this study, an inhibitor-tolerant strain of A. niger (Aspergillus niger) was used for the biological detoxification of corncob hydrolysate. The results showed that A. niger M13 can tolerate a concentration of at least 7.50 ± 0.19 g/L of acetic acid, 1.81 ± 0.13 g/L of furfural, and 1.02 ± 0.10 g/L of HMF (5-Hydroxymethylfurfural). The spores had a higher detoxification efficiency than the mycelial pellets with a detoxification rate of 0.1566 g/L/h, 0.1125 g/L/h, and 0.015 g/L/h for acetic acid, furfural, and HMF, respectively. The cell preferentially consumed furfural, then HMF, before simultaneously degrading acetic acid and glucose. A. niger M13 spores could accumulate small amounts of citric acid directly from undetoxified hydrolysate at a concentration of about 6 g/L. Therefore, A. niger M13 can serve as an excellent biological detoxification strain and a potential citric acid fermenting strain when using undetoxified lignocellulosic hydrolysates.
Assessment of the Impact of Coffee Waste as an Alternative Feed Supplementation on Rumen Fermentation and Methane Emissions in an In Vitro Study
Authors: Batbekh Belgutei , Ahmed Eslam , Hanada Masaaki , Fukuma Naoki , Nishida Takehiro
Published: September 2023

Spent coffee waste is the most common by-product of coffee processing, and it has the potential to be used as a source of organic compounds for ruminant diets. The objective of this study was to evaluate the optimal inclusion level and method for using spent coffee waste (SCW) as a ruminant feed and investigate its effects on rumen fermentation characteristics and methane (CH4) production. The present in vitro batch culture study was conducted using two different experimental designs. The first experimental design (TRIAL. 1) was performed using a control diet of 500 mg of fresh matter basal diet (60% hay/40% concentrate), with SCW being used as a feed additive at 1%, 10% and 20% of the substrate. The second experimental design was performed using the same control diet, with spent coffee waste replacing either part of the hay (TRIAL. 2) or some of the concentrate mixture (TRIAL. 3) at four different dosages (30:70, 50:50, 70:30 and 100). When SCW was supplemented as a feed additive, there were increases in the production of volatile fatty acids and gas; however, it did not show any suppressive effects on CH4 production. In contrast, when SCW was included as a replacement for hay or concentrate, there were significant reductions in CH4 production with increasing levels of SCW inclusion. These reductions in CH4 production were accompanied by negative effects on nutrient digestibility and total volatile fatty acid production. These findings demonstrate that SCW could potentially be used as a prebiotic feed additive. Additionally, when SCW is used as a replacement for silage at 70:30 and 50:50 dosages appear to be feasible as a substitute for animal feed (hay and concentrate).
Technical, Qualitative and Energy Analysis of Wireless Control Modules for Distributed Smart Home Systems
Authors: Ożadowicz Andrzej
Published: September 2023

Distributed smart home systems using wireless communication are increasingly installed and operated in households. Their popularity is due to the ease of installation and configuration. This paper presents a comprehensive technical, quality, and energy analysis of several popular smart home modules. Specifically, it focuses on verifying their power consumption levels, both in standby and active mode, to assess their impact on the energy efficiency of building installations. This is an important aspect in the context of their continuous operation, as well as in relation to the relatively lower power of loads popular in buildings, such as LED lighting. The author presents the results of measurements carried out for seven different smart home modules controlling seven different types of loads. The analysis of the results shows a significant share of home automation modules in the energy balance; in particular, the appearance of reactive power consumption due to the installation of smart home modules is noteworthy. Bearing in mind all the threads of the analysis and discussion of the results of measurement experiments, a short SWOT analysis is presented, with an indication of important issues in the context of further development of smart systems and the Internet of Things with wireless communication interfaces, dedicated to home and building applications.
Surface Radiative Forcing as a Climate-Change Indicator in North India due to the Combined Effects of Dust and Biomass Burning
Authors: Dumka Umesh Chandra , Kosmopoulos Panagiotis G. , Baxevanaki Effrosyni , Kaskaoutis Dimitris G. , Huda Muhammad Nurul , Khan Md Firoz , Bilal Muhammad , Ambade Balram , Khanal Sujan , Munshi Pavel
Published: September 2023

This study estimates the radiative forcing by biomass burning and dust aerosols over the Indian subcontinent, with emphasis on the Indo-Gangetic Plains (IGP) during the period from January 2021 to April 2021, based on multiple satellite and reanalysis datasets. In this respect, we used retrievals from the Moderate Resolution Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) system, as well as reanalysis data from the Goddard Earth Observing System, version 5 (GEOS-5), the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), the Copernicus Atmosphere Monitoring Service (CAMS), and ERA-Interim. According to the MERRA-2 and the CAMS, the highest black carbon (BC) concentrations in January 2021 were 7–8 µg m−3, which were significantly lower than measurements performed in main cities along the IGP, such as Patiala, Delhi, and Kanpur. The meteorological data analysis accompanied by the CALIPSO lidar measurements showed that the vertical distribution of total attenuated backscatter (TAB) could reach altitudes of up to ~4–5 km and could be transported over the central Himalayan region. The spatial-averaged daily aerosol radiative forcing (ARF) values over the Indian subcontinent from January 2021 to April 2021 were found to range from −51.40 to −6.08 W m−2 (mean of −22.02 ± 9.19 W m−2), while on a monthly basis, the ARF values varied widely, from −146.24 to −1.63 W m−2 (mean of −45.56 ± 22.85 W m−2) over different parts of the study region. Furthermore, the spatial-averaged daily BC radiative forcing ranged from −2.23 to −0.35 (−1.01 ± 0.40 W m−2), while it varied from −15.29 to −0.31 W m−2 (−2.46 ± 2.32 W m−2) over different regions of southern Asia, indicating a rather small contribution to the total aerosol radiative effect and a large presence of highly scattering aerosols. Our findings highlight the importance of growing biomass burning, in light of recent climate change and the rapid decline in air quality over North India and the Indian Ocean.
Exploring Prescribed Fire Severity Effects on Ground Beetle (Coleoptera: Carabidae) Taxonomic and Functional Community Composition
Authors: Mason Stephen C. , Shirey Vaughn , Waite Evan S. , Gallagher Michael R. , Skowronski Nicholas S.
Published: September 2023

Prescribed fire is a management tool that is frequently used to foster biodiversity. Simultaneously, insects that provide essential ecosystem services are globally declining. Within the pyroentomology literature, there are mixed reports of positive and negative effects that prescribed fires have on insect communities. This is likely due to not accounting for fire heterogeneity created by fire severity. To better understand prescribed fire severity effects on insect communities, we used multispectral reflectance data collected by Sentinel-2 to methodically quantify prescribed fire severity and compared ground beetle (Coleoptera: Carabidae) taxonomic and functional community composition responses between an unburned site and two burned sites with contrasting fire impacts. We found 23 ground beetle species and used 30 morphological, physiological, phenological, and ecological functional traits for each species. We found that our moderate fire severity site had different taxonomic and functional community compositions from both our unburned and high-severity sites. Surprisingly, we did not find a strong difference in taxonomic or functional ground beetle composition between our unburned and high-severity sites. Our results encourage future pyroentomology studies to account for fire severity, which will help guide conservation managers to make more accurate decisions and predictions about prescribed fire effects on insect biodiversity.
Recycling Fish Heads for the Production of Prodigiosin, a Novel Fungicide via Experimental and Molecular Docking Characterization
Authors: Nguyen Van Bon , Wang San-Lang , Nguyen Thi Hanh , Phan Tu Quy , Nguyen Thi Huyen , Tran Thi Ha Trang , Doan Manh Dung , Ngo Van Anh , Nguyen Anh Dzung
Published: September 2023

This report aims to characterize and reuse fish head powder (FHP) as a potential carbon/nitrogen source for microbial fermentation to produce prodigiosin (PG), and to evaluate its novel anti-fungal effect via an experimental and docking study. FHP has high protein (30.25%) and lipid (5.12%) content and a diversity of elements, which suggests that FHP is a potential substrate for microbial fermentation. Serratia marcescens TNU01 was screened as the most effective strain to produce PG from FHP. Further experiments indicated that the combination of FHP and crude chitin at the ratio of 9/1 and used at 1.5% as a C/N source for fermentation significantly enhanced PG production. Supplementing a minor amount of 0.05% Ca3(PO4)2, 0.03% K2SO4 also positively affects PG yield production. The fermentation using a bioreactor system (type of 14 L) achieved the highest PG yield production (6.4 mg/mL) in a short cultivation time (10 h) in comparison with the highest PG yield (5.8 mg/mL) obtained after 36 h of fermentation in a flask. In the anti-fungal evaluation of various plant pathogen fungal strains, PG demonstrated efficacy against Fusarium solani F04. The molecular docking results also showed that PG effectively interacted with targeting protein 3QPC of F. solani with good binding energy (DS—9.2 kcal/mol) and an acceptable RMSD value (0.94 Å). In this study, we provide a novel application of FHP for the eco-friendly production of PG, which can potentially used for the management of Fusarium solani.
Influence of Variety, Enzyme Addition and Destemming on Yield and Bioactive Compounds of Juices from Selected Hybrid Grape Varieties Cultivated in Poland
Authors: Zubaidi Muhamad Alfiyan , Czaplicka Marta , Kolniak-Ostek Joanna , Nawirska-Olszańska Agnieszka
Published: September 2023

In this study, five varieties of hybrid grapes were processed into juice to assess the influence of variety, destemming process and enzyme addition on juice quality, including yield, physicochemical properties and bioactive compounds. The results highlighted that while the processing methods had some impact on juice quality, the inherent grape variety remained the most significant factor. Although similar treatments were applied to all varieties, there were differences in the quality attributes of the juice. In general, red grape juice had a higher polyphenol content and antioxidant capacity than white grape juice. Four phenolic acids, eleven flavonols, five flavan-3-ols and five anthocyanins were identified. While the polyphenolic profile of each sample depended on the treatment and the variety, regardless of the variety, destemming was found to increase the yield by around 10–15%, while the addition of enzyme improved the yield by around 20–30%. Although the addition of enzymes led to a higher polyphenol content, it changed the color profile of the juice as a result of the pigment’s skin extraction. In contrast, the destemming process did not influence the color of the juice, but at the same time, it reduced the health benefits due to the removal of potential bioactive compounds from the stems.
Discovery of ACE Inhibitory Peptides Derived from Green Coffee Using In Silico and In Vitro Methods
Authors: Dai Haopeng , He Min , Hu Guilin , Li Zhongrong , Al-Romaima Abdulbaset , Wu Zhouwei , Liu Xiaocui , Qiu Minghua
Published: September 2023

Inhibition of angiotensin-I converting enzyme (ACE) is an important means of treating hypertension since it plays an important regulatory function in the renin-angiotensin system. The aim of this study was to investigate the ACE inhibitory effect of bioactive peptides from green coffee beans using in silico and in vitro methods. Alcalase and thermolysin were employed to hydrolyze protein extract from coffee beans. Bioactive peptides were identified by LC-MS/MS analysis coupled with database searching. The potential bioactivities of peptides were predicted by in silico screening, among which five novel peptides may have ACE inhibitory activity. In vitro assay was carried out to determine the ACE inhibitory degree. Two peptides (IIPNEVY, ITPPVMLPP) were obtained with IC50 values of 57.54 and 40.37 μM, respectively. Furthermore, it was found that two inhibitors bound to the receptor protein on similar sites near the S1 active pocket of ACE to form stable enzyme–peptide complexes through molecular docking, and the Lineweaver–Burk plot showed that IIPNEVY was a noncompetitive inhibitor, and ITPPVMLPP was suggested to be a mixed-type inhibitor. Our study demonstrated that two peptides isolated from coffee have potential applications as antihypertensive agents.
Enzymatically Hydrolyzed Poultry By-Product Supplementation, Instead of Fishmeal, Alone Improves the Quality of Largemouth Bass (Micropterus salmoides) Back Muscle without Compromising Growth
Authors: Yi Changguo , Huang Dongyu , Yu Heng , Gu Jiaze , Liang Hualiang , Ren Mingchun
Published: September 2023

This study was designed to investigate the effects of enzymatically hydrolyzed poultry by-products (EHPB) on the growth and muscle quality of largemouth bass. Different concentrations of EHPB (0.00, 3.10, 6.20, 9.30, and 12.40%) were added to replace fishmeal (0.00 (control), 8.89 (EHPB1), 17.78 (EHPB2), 26.67 (EHPB3), and 35.56% (EHPB4)), respectively, in dietary supplementation. The results revealed that the growth performance and muscle amino acid and fatty acid remained unaltered in EHPB1 (p > 0.05). EHPB1 showed significant reduction in muscle hardness, gumminess, chewiness, and muscle fiber count and exhibited a significant increase in muscle fiber volume. The decrease in muscle hardness, gumminess, and chewiness means that the muscle can have a more tender texture. The expression of protein metabolism-related genes reached the highest levels in EHPB1 and EHPB2 (p < 0.05). The mRNA levels of s6k and igf-1 in EHPB2 and EHPB1 were significantly lower than those in the control group. Compared to the control group, the expression of muscle production-associated genes paxbp-1 was higher in EHPB1, and myod-1, myf-5, and syndecan-4 were higher in EHPB2. The mRNA levels of muscle atrophy-related genes, in EHPB4 and EHPB2, were significantly lower than those in the control group. Therefore, the EHPB1 group plays a role in promoting the expression of genes related to muscle formation. In summary, replacing 8.89% of fishmeal with EHPB in feed has no effect on growth and may improve back muscle quality in largemouth bass.
Identification and Characterization of Novel Malto-Oligosaccharide-Forming Amylase AmyCf from Cystobacter sp. Strain CF23
Authors: Wang Jihong , Zhang Lei , Wang Peiwen , Lei Jinhui , Zhong Lingli , Zhan Lei , Ye Xianfeng , Huang Yan , Luo Xue , Cui Zhongli , Li Zhoukun
Published: September 2023

Malto-oligosaccharides (MOSs) from starch conversion is advantageous for food and pharmaceutical applications. In this study, an efficient malto-oligosaccharide-forming α-amylase AmyCf was identified from myxobacter Cystobacter sp. strain CF23. AmyCf is composed of 417 amino acids with N-terminal 41 amino acids as the signal peptide, and conserved glycoside hydrolase family 13 (GH13) catalytic module and predicted C-terminal domain with β-sheet structure are also identified. Phylogenetic and functional analysis demonstrated that AmyCf is a novel member of GH13_6 subfamily. The special activity of AmyCf toward soluble starch and raw wheat starch is 9249 U/mg and 11 U/mg, respectively. AmyCf has broad substrate specificity toward different types of starches without requiring Ca2+. Under ideal circumstances of 60 °C and pH 7.0, AmyCf hydrolyzes gelatinized starch into maltose and maltotriose and maltotetraose as the main hydrolytic products with more than 80% purity, while maltose and maltotriose are mainly produced from the hydrolysis of raw wheat starch with more than 95% purity. The potential applicability of AmyCf in starch processing is highlighted by its capacity to convert gelatinized starch and raw starch granules into MOSs. This enzymatic conversion technique shows promise for the low-temperature enzymatic conversion of raw starch.
Association between Dietary Patterns and Metabolic Syndrome Risk Factors: A Cross-Sectional Study of Christian Orthodox Church Fasters and Non-Fasters in Greece
Authors: Kokkinopoulou Anna , Katsiki Niki , Pagkalos Ioannis , Rodopaios Nikolaos E. , Koulouri Alexandra-Aikaterini , Vasara Eleni , Papadopoulou Sousana K. , Skepastianos Petros , Hassapidou Maria , Kafatos Anthony G.
Published: September 2023

It is well known that the Mediterranean diet contributes to healthy living, prevention of non-communicable diseases, and longevity. A cross-sectional study was conducted with participants from Greece who follow the Mediterranean diet and were further divided into two categories: (i) Christian Orthodox Church (COC) religious fasters and (ii) non-fasters. Four-hundred individuals underwent anthropometric measurements, whereas information regarding dietary intake was collected via three 24 h diet recalls and a monthly food frequency questionnaire. Principal component analysis was performed to derive dietary patterns, whereas associations between dietary patterns and metabolic syndrome (MetS) risk factors were investigated with the general linear model. Non-fasters (n = 200) were found to consume significantly more beef, chicken, turkey, sausage, broth, fried potatoes, ketchup, and mustard, while consuming less seafood, snails, soya, tarama salads, fresh fruits, margarine, olives, and decaf coffee. Two distinct dietary patterns were identified in fasters: (i) the “Mixed Diet” and (ii) the “Plant-based/Fasting Diet” pattern. Moreover, three patterns were identified in non-fasters, and were identified as follows: (i) the “Western Diet”, (ii) the “Mixed Diet”, and (iii) the “Mediterranean-like Diet” pattern. No significant association was observed between dietary patterns and the prevalence of MetS in our population. Further epidemiological studies should evaluate the links between dietary patterns and MetS prevalence within the adult Greek population.
New Insights into Surgical Management of Intrahepatic Cholangiocarcinoma in the Era of “Transplant Oncology”
Authors: Melandro Fabio , Ghinolfi Davide , Gallo Gaetano , Quaresima Silvia , Nasto Riccardo Aurelio , Rossi Massimo , Mennini Gianluca , Lai Quirino
Published: September 2023

Intrahepatic cholangiocarcinoma (iCCA) represents the second most frequent type of primary liver neoplasm. The diagnosis and treatment of patients with iCCA involves many challenges. To date, surgical resection with negative margins is the main curative option, achieving an acceptable long-term survival. Despite enabling a considerable improvement in the outcome, iCCA recurrence after surgery is still common. Tumor extension and the histological subtype, as well as vascular and lymph node involvements, are key factors used to define the prognosis. In this narrative review, we aimed to discuss the potential benefits of using different surgical strategies in the field of iCCA, including vascular resection, the mini-invasive approach, liver transplantation, the mechanism used to enable future liver remnant augmentation, and lymph node dissection. We also discussed the new protocols developed in the field of systemic treatment, including immunotherapy and molecular targeted therapy. Recent advancements in the diagnosis, surgical treatment, and understanding of tumor biology have changed the landscape in terms of treatment options. Creating a multidisciplinary tumor board is essential to achieving the best patient outcomes. Further investigational trials are required with the intent of tailoring the treatments and establishing the right patient population who would benefit from the use of new therapeutics algorithms.