bluuebunny/crossref_metadata_2025 huggingface dataset:
A Survey of Explainable Artificial Intelligence for Smart Cities
Authors: Javed Abdul Rehman , Ahmed Waqas , Pandya Sharnil , Maddikunta Praveen Kumar Reddy , Alazab Mamoun , Gadekallu Thippa Reddy
Published: February 2023

The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities.
A Fractional-Order Multi-Rate Repetitive Controller for Single-Phase Grid-Connected Inverters
Authors: Zhao Qiangsong , Liu Kaiyue , Li Hengyi
Published: February 2023

The multi-rate repetitive controller (MRC) can achieve zero steady-state error in tracking the reference current signal of grid-connected inverters, save the settling time effectively, and improve the running speed. However, when the grid frequency fluctuates, the harmonic suppression performance of MRC will degrade. Aiming at the problem of harmonic suppression performance degradation, a fractional-order MRC (FOMRC) based on the farrow structure fractional delay (FD) filter is proposed. Firstly, the equivalent digital model of MRC is established, and a Farrow structure fractional delay (FD) filter based on Taylor series expansion is selected as the internal model filter of MRC. The stability analysis and harmonic suppression characteristics of the FOMRC are analyzed. Then, the parameter design of FOMRC applied to an LCL single-phase grid-connected inverter control system is given. Finally, the simulation results show that the proposed method has better transient and steady-state performance than the CRC when the grid frequency fluctuates.
Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on an Improved Swin Transformer
Authors: Sun Ruina , Pang Yuexin , Li Wenfa
Published: February 2023

With the advancement of computer technology, transformer models have been applied to the field of computer vision (CV) after their success in natural language processing (NLP). In today’s rapidly evolving medical field, radiologists continue to face multiple challenges, such as increased workload and increased diagnostic demands. The accuracy of traditional lung cancer detection methods still needs to be improved, especially in realistic diagnostic scenarios. In this study, we evaluated the performance of the Swin Transformer model in the classification and segmentation of lung cancer. The results showed that the pre-trained Swin-B model achieved a top-1 accuracy of 82.26% in the classification mission, outperforming ViT by 2.529%. In the segmentation mission, the Swin-S model demonstrated improvement over other methods in terms of mean Intersection over Union (mIoU). These results suggest that pre-training can be an effective approach for improving the accuracy of the Swin Transformer model in these tasks.
Review and Development of a Scalable Lightweight Blockchain Integrated Model (LightBlock) for IoT Applications
Authors: Mahmoud Moamin A. , Gurunathan Mathuri , Ramli Ramona , Babatunde Kazeem Alasinrin , Faisal Faisal Hadi
Published: February 2023

The Internet of things (IoT) is on the rise, and it is increasingly becoming a ubiquitous computing service that requires large data storage and processing. Due to the limited capabilities of most IoT devices, they always rely on cloud services for storage and computing, posing security and privacy risks. Due to its significant aspects, such as data security and privacy, Blockchain (BC) has drawn significant interest in alleviating these concerns. Despite its potential benefits, Blockchain’s high complexity and limited scalability make it computationally expensive and incompatible with IoT devices requiring lightweight solutions. Although several models have been proposed for this purpose, the issue is still far from being resolved. These models have significant constraints, such as resource optimization during operation. By presenting a scalable lightweight Blockchain integrated model (LightBlock), the primary objective of this work is to enable the usage of BC in the IoT environment. LightBlock is implemented by optimizing the components of a lightweight, scalable Blockchain. Lastly, the study’s findings should provide IoT users with a high level of security and privacy by ensuring applicability and offering end-to-end security.
Reliability Analysis of Failure-Dependent System Based on Bayesian Network and Fuzzy Inference Model
Authors: Xiang Shangjia , Lv Yaqiong , Li Yifan , Qian Lu
Published: February 2023

With the rapid development of information and automation technology, the manufacturing system is evolving towards more complexity and integration. The system components will inevitably suffer from degeneration, and the impact of component-level failure on the system reliability is a valuable issue to be studied, especially when failure dependence exists among the components. Thus, it is vital to construct a system reliability evaluation mechanism that helps to characterize the healthy status of the system and facilitate wise decision making. In this paper, a reliability analysis framework for a failure-dependent system is proposed, in which copula functions with optimized parameters are used for the description of different failure correlations, and a fuzzy inference model is constructed to derive the subsystem reliability based on the component-level failure correlation. Finally, a Bayesian network is applied to infer the system reliability based on the system structure combined with the impact of failure correlation inside. Simulation results of the proposed method show that the inference results of system reliability are reasonable and effective in different cases. Compared with the copula Bayesian network method, the proposed method shows better adaptability to failure-dependent systems to varying degrees. This work can provide theoretical guidance for evaluating the reliability of manufacturing systems of different types.
Machine-Learning-Based IoT–Edge Computing Healthcare Solutions
Authors: Alnaim Abdulrahman K. , Alwakeel Ahmed M.
Published: February 2023

The data that medical sensors collect can be overwhelming, making it challenging to glean the most relevant insights. An algorithm for a body sensor network is needed for the purpose of spotting outliers in the collected data. Methods of machine learning and statistical sampling can be used in the research process. Real-time response optimization is a growing field, as more and more computationally intensive tasks are offloaded to the backend. Optimizing data transfers is a topic of study. Computing power is dispersed across many domains. Computation will become a network bottleneck as more and more devices gain Internet-of-Things capabilities. It is crucial to employ both task-level parallelism and distributed computing. To avoid running down the battery, the typical solution is to send the processing to a server in the background. The widespread deployment of Internet-of-Things (IoT) devices has raised serious privacy and security concerns among people everywhere. The rapid expansion of cyber threats has rendered our current privacy and security measures inadequate. Machine learning (ML) methods are gaining popularity because of the reliability of the results that they produce, which can be used to anticipate and detect vulnerabilities in Internet-of-Things-based systems. Network response times are improved by edge computing, which also increases decentralization and security. Edge nodes, which frequently communicate with the cloud, can now handle a sizable portion of mission-critical computation. Real-time, highly efficient solutions are possible with the help of this technology. To this end, we use a distributed-edge-computing-based Internet-of-Things (IoT) framework to investigate how cloud and edge computing can be combined with ML. IoT devices with sensor frameworks can collect massive amounts of data for subsequent analysis. The front-end component can benefit from some forethought in determining what information is most crucial. To accomplish this, an IoT server in the background can offer advice and direction. The idea is to use machine learning in the backend servers to find data signatures of interest. We intend to use the following ideas in the medical field as a case study. Using a distributed-edge-computing-based Internet-of-Things (IoT) framework, we are investigating how to combine the strengths of both cloud and edge computing with those of machine learning.
Bifurcation Phenomena in Open-Loop DCM-Operated DC–DC Switching Converters Feeding Constant Power Loads
Authors: El Aroudi Abdelali , Benadero Luis , Haroun Reham , Martínez-Salamero Luis , Tse Chi K.
Published: February 2023

Constant power loading is an effect that appears in multiple-stage energy conversion systems with individually regulated switching power converters. In a two-stage system, an upstream or source converter drives one or more downstream or load converters. The downstream converters in a two-stage power conversion system are designed to provide the fastest transient response in stand-alone operation. Consequently, they behave as a constant power load (CPL) to the upstream converter within their control bandwidth. In the past, open-loop power converters feeding CPLs and operating in discontinuous conduction mode (DCM) were considered to be stable. In this paper, it is shown that these systems can undergo instabilities, which have been so far overlooked. First, numerical simulations from the switched model of an open-loop boost converter under DCM operation and loaded with a tightly regulated buck converter and the same converter loaded by an ideal CPL are presented to show that they exhibit similar nonlinear behavior and bifurcation phenomena. Then, the three elementary open-loop DC–DC converters operating in the DCM were considered and their bifurcation phenomena were revealed. It is shown that the period-doubling route to chaos in the DC–DC boost converter is interrupted by a sudden appearance of dangerous destructive dynamics due to the excessively unlimited load current in the CPL. For the buck converter, only the first period-doubling bifurcation is observed before the destructive behavior appears. The open-loop buck–boost converter under DCM and feeding a CPL is always unstable and exhibits no periodic orbit. Based on the observed phenomena, approximate discrete-time models were derived, which despite their simplicity, were seen to display the most-important and -essential features of the corresponding switching converters before destructive dynamics occurs.
MWIRGAN: Unsupervised Visible-to-MWIR Image Translation with Generative Adversarial Network
Authors: Uddin Mohammad Shahab , Kwan Chiman , Li Jiang
Published: February 2023

Unsupervised image-to-image translation techniques have been used in many applications, including visible-to-Long-Wave Infrared (visible-to-LWIR) image translation, but very few papers have explored visible-to-Mid-Wave Infrared (visible-to-MWIR) image translation. In this paper, we investigated unsupervised visible-to-MWIR image translation using generative adversarial networks (GANs). We proposed a new model named MWIRGAN for visible-to-MWIR image translation in a fully unsupervised manner. We utilized a perceptual loss to leverage shape identification and location changes of the objects in the translation. The experimental results showed that MWIRGAN was capable of visible-to-MWIR image translation while preserving the object’s shape with proper enhancement in the translated images and outperformed several competing state-of-the-art models. In addition, we customized the proposed model to convert game-engine-generated (a commercial software) images to MWIR images. The quantitative results showed that our proposed method could effectively generate MWIR images from game-engine-generated images, greatly benefiting MWIR data augmentation.
Using a Light-Weight CNN for Perfume Identification with An Integrated Handheld Electronic Nose
Authors: Cao Mengli
Published: February 2023

Exposing counterfeit perfume products is significant for protecting the legal profit of genuine perfume manufacturers and the health of perfume consumers. As a holistic solution to the problem of perfume identification (PI) using an electronic nose (EN), the methods based on convolutional neural network (CNN) simplifies the inconvenient selection of methods and parameter values, which has traditionally complicated existing combinatory methods. However, existing CNN methods that can be used for EN-based PI were designed on the premise that the CNN model can be trained with plenty of computational resources in divide-body ENs. Aiming at PI with an integrated handheld EN, a novel light-weight CNN method, namely LwCNN, is presented for being entirely conducted on a resource-constrained NVDIA Jetson nano module. LwCNN utilizes a sequenced stack of two feature flattening layers, two one-dimensional (1D) convolutional layers, a 1D max-pooling layer, a feature dropout layer, and a fully connected layer. Extensive real experiments were conducted on an integrated handheld EN to the performance of LwCNN with those of four existing benchmark methods. Experimental results show that LwCNN obtained an average identification accuracy of 98.35% with model training time of about 26 s.
Timeslot Scheduling with Reinforcement Learning Using a Double Deep Q-Network
Authors: Ryu Jihye , Kwon Juhyeok , Ryoo Jeong-Dong , Cheung Taesik , Joung Jinoo
Published: February 2023

Adopting reinforcement learning in the network scheduling area is getting more attention than ever because of its flexibility in adapting to the dynamic changes of network traffic and network status. In this study, a timeslot scheduling algorithm for traffic, with similar requirements but different priorities, is designed using a double deep q-network (DDQN), a reinforcement learning algorithm. To evaluate the behavior of the DDQN agent, a reward function is defined based on the difference between the estimated delay and the deadline of packets transmitted at the timeslot, and on the priority of packets. The simulation showed that the designed scheduling algorithm performs better than existing algorithms, such as the strict priority (SP) or weighted round robin (WRR) scheduler, in the sense that more packets arrived within the deadline. By using the proposed DDQN-based scheduler, it is expected that autonomous network scheduling can be realized in upcoming frameworks, such as time-sensitive or deterministic networking.
ABMM: Arabic BERT-Mini Model for Hate-Speech Detection on Social Media
Authors: Almaliki Malik , Almars Abdulqader M. , Gad Ibrahim , Atlam El-Sayed
Published: February 2023

Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. This paper aims to address this issue by proposing an effective approach for detecting Arabic hate speech on social media platforms, namely Twitter. Therefore, this paper introduces the Arabic BERT-Mini Model (ABMM) to identify hate speech on social media. More specifically, the bidirectional encoder representations from transformers (BERT) model was employed to analyze data collected from Twitter and classify the results into three categories: normal, abuse, and hate speech. In order to evaluate our model and state-of-the-art approaches, we conducted a series of experiments on Twitter data. In comparison with previous works on Arabic hate-speech detection, the ABMM model shows very promising results with an accuracy score of 0.986 compared to the other models.
Countermeasuring MITM Attacks in Solar-Powered PON-Based FiWi Access Networks
Authors: Tsompanoglou Polyxeni , Iliadis Antonios , Kantelis Konstantinos , Petridou Sophia , Nicopolitidis Petros
Published: February 2023

Solar power (SP) passive optical network (PON)-based fiber-wireless (FiWi) access systems are becoming increasingly popular as they provide coverage to rural and urban areas where no power grid exists. Secure operation of such networks which includes solar- and/or battery-powered devices, is crucial for anticipating potential network issues and prolong the life of the network operation. Since optical network units (ONUs) may be powered by SP-charged batteries, energy awareness becomes an important issue, particularly when it comes to reducing ONUs’ energy consumption and allowing them to operate in off-grid remote areas. With the PON as the fixed part of these networks, the optical line terminal (OLT) informs the ONUs through a message exchange mechanism when no traffic is present, allowing them to transition to a low-power-consumption sleep mode. However, man-in-the-middle (MITM) attacks pose a serious threat to the message exchange mechanisms, which can eventually drain the energy of battery-powered ONUs resulting in their shutdown. Consequently, this paper introduces two novel mechanisms for reducing ONU energy consumption, namely the wake-up and time-out mechanisms, which can be used to mitigate the effectiveness of MITM attacks that may seek to affect the unit’s operation due to battery drain. The formal verification results show that these goals were effectively achieved.
Performance Assessment of Cellulose Paper Impregnated in Nanofluid for Power Transformer Insulation Application: A Review
Authors: Adekunle Andrew Adewunmi , Oparanti Samson Okikiola , Fofana Issouf
Published: February 2023

Insulation cellulose paper is a basic measure for a power transformer’s remaining useful life, and its advantageous low cost, electrical, and mechanical properties have made it an extensive insulation system when impregnated in a dielectric liquid. Cellulose paper deteriorates as a result of ageing due to some chemical reactions like pyrolysis (heat), hydrolysis (moisture), and oxidation (oxygen) that affects its degree of polymerization. The condition analysis of cellulose paper has been a major concern since the collection of paper samples from an operational power transformer is almost impossible. However, some chemicals generated during cellulose paper deterioration, which were dissolved in dielectric liquid, have been used alternatively for this purpose as they show a direct correlation with the paper’s degree of polymerization. Furthermore, online and non-destructive measurement of the degree of polymerization by optical sensors has been proposed recently but is yet to be available in the market and is yet generally acceptable. In mitigating the magnitude of paper deterioration, some ageing assessments have been proposed. Furthermore, researchers have successfully enhanced the insulating performance of oil-impregnated insulation paper by the addition of various types of nanoparticles. This study reviews the ageing assessment of oil-paper composite insulation and the effect of nanoparticles on tensile strength and electrical properties of oil-impregnated paper insulation. It includes not only significant tutorial elements but also some analyses, which open the door for further research on the topic.
A Zero-Carbon Nuclear Energy Future? Lessons Learned from Perceptions of Climate Change and Nuclear Waste
Authors: Dehner Grace , McBeth Mark K. , Moss Rae , van Woerden Irene
Published: February 2023

Nuclear energy is proposed as part of the solution to a net-zero carbon future. However, environmental issues with nuclear energy remain. In this study, a total of 1616 participants from across the U.S. stated their position on the following statements: “Nuclear energy is a clean energy source”, “Nuclear energy may be part of the solution to climate change”, “I am willing to accept the building of new nuclear power stations if it is environmentally friendly and had a zero-carbon footprint”, and “Nuclear power may lead to more pollution and environmental contamination”. Participants were also asked “Do you think nuclear energy is a zero-carbon energy?” Logistic regression was used to determine how concern around climate change and nuclear waste predicted participant responses. Latent class analysis (LCA) was used to determine segments of respondents based on their perceptions of nuclear energy and the environment. Nuclear energy was perceived as being zero-carbon (74% agree), but not necessarily clean (50% agree). Nuclear energy was perceived as part of the solution to climate change (51% agree), but concern around more pollution and environmental contamination remained (42% agree). Concern around climate change was associated with greater odds of acceptance of nuclear energy, while concern around nuclear waste was associated with the opposite. The LCA suggested a “favorable”, “neutral”, and “negative” class, for which approximately 40%, 52%, and 8% of participants, respectively, belonged. This study suggests conditional (or reluctant) support for nuclear energy is occurring.
Structural and Investment Funds of the European Union as an Instrument for Creating a Low-Carbon Economy by Selected Companies of the Energy Sector in Poland
Authors: Dembicka-Niemiec Agnieszka , Szafranek-Stefaniuk Edyta , Kalinichenko Antonina
Published: February 2023

The main objective of this research is to identify the scope of the use of EU funds for the formation of a low-carbon economy by enterprises providing energy services in Poland in 2014–2020. As a result of the identification, a model for the use of EU funds based on the following criteria was identified: the purpose of the investment, the type of fund, the type of support program, the range of support values and the form and level of funding. As a research gap has been identified due to the insufficient investigation of the use of EU funds by the largest energy companies in Poland to shape a low-carbon economy, the findings presented are novel and contribute to a better understanding of the use of EU funds by Poland’s largest energy sector companies. Data on investment projects financed by EU funds were obtained from the database of the Ministry of Funds and Regional Policy for 2014–2020, while the characteristics of the companies were obtained from industry reports, the National Court Register and the Central Statistical Office. The results showed that EU funds were important in the financing of investments by the largest energy companies to decarbonize the economy. The analysis showed that the surveyed companies were pursuing the goals of Directive 2012/27/EU of the European Parliament and of the Council for energy efficiency, primarily concerning a low-carbon economy. Most EU aid funds were used for infrastructure investments, including those related to renewable energy sources. Little use has been made of EU funds for innovation and public awareness.
Positive Impact of Red Soil on Albedo and the Annual Yield of Bifacial Photovoltaic Systems in Ghana
Authors: Grommes Eva-Maria , Blieske Ulf , Hadji-Minaglou Jean-Régis
Published: February 2023

The annual yield of bifacial photovoltaic systems is highly dependent on the albedo of the underlying soil. There are currently no published data about the albedo of red soil in western Africa. In this study, the impact of the albedo of red soil in Ghana on the energy yield of bifacial photovoltaic systems is analysed. A bifacial photovoltaic simulation model is created by combining the optical view factor matrix with an electrical output simulation. For an exact simulation, the albedo of red soil at three different locations in Ghana is measured for the first time. The average albedo of every red soil is clearly determined, as well as the measurement span including instrumentation uncertainty; values between 0.175 and 0.335 were measured. Considering these data, a state-of-the-art bifacial photovoltaic system with an average of 19.8% efficient modules in northern Ghana can achieve an annual energy yield of 508.8 kWh/m2 and a bifacial gain of up to 18.3% in comparison with monofacial photovoltaic panels. To summarise, red soil in two out of three locations in Ghana shows higher albedo values than most natural ground surfaces and therefore positively impacts the annual yield of bifacial photovoltaic systems.
Linear Model for Two-Layer Porous Bed Suspended with Nano Sized Particles
Authors: Umavathi Jawali C. , Sheremet Mikhail A.
Published: February 2023

Two immiscible fluids flows are materialized in science and technology; the combined convection of the two immiscible fluids in a square conduit is reviewed in this study. The nanofluid and pure viscous fluid which do not mix are discussed, and both layers saturated with a porous matrix have different permeabilities. The Dupuit–Forchheimer and Tiwari–Das models are applied to outline the permeability of the layer and nanofluids, respectively. The finite difference method is utilized to find the solutions of conservation equations along with suitable boundary and interface conditions. The boundary condition for the velocity is no slip at all the boundaries, while continuity of velocity and shear stress are used at the interface. The left and right walls are kept at constant but different temperatures, the top and bottom walls are isolated, and the continuity of temperature and heat flux is assumed at the interface. Grashof number, Brinkman number, Darcy number, inertia parameter, permeability ratio, solid volume fraction, thermal conductivity and viscosity ratios, different nanoparticles, and various base liquids of the two-layered fluids are engaged. The velocity is depleted by the inertia and viscosity ratio while it is accelerated with the Darcy and Grashof numbers. The energy distribution was not modulated significantly with any of the dimensionless numbers. Using copper nanoparticles doped in mineral oil and ethylene glycol produced the peak momentum. Diamond nanoparticles dropped in water catalysis showed the best heat transfer rate.
Energetic, Economic and Environmental (3E) Analysis of a RES-Waste Gasification Plant with Syngas Storage Cooperation
Authors: Roman Jacek , Wróblewski Robert , Klojzy-Karczmarczyk Beata , Ceran Bartosz
Published: February 2023

Today, the increasing amount of waste is a growing ecological and financial problem. Another issue is the need to limit the share of controllable sources powered by fossil fuels. A hybrid generation system (HGS) is proposed to solve both problems. The system consists of renewable energy sources (RES) and a waste gasification system. Contrary to many papers, it is proposed to include syngas storage and use gas turbines as balancing sources. The HGS was modeled, and electricity generation, capacity factors, and efficiencies were calculated. The economic (LCOE and PP) and environmental parameters (CO2 emission and reduction) were analyzed and calculated for different RES capacities. The results show that the proposed HGS covered 45.7–80% of municipal demand. The HGS was characterized by high CO2 emissions, due to the low efficiency of gasification-gas turbine installation and the need to compress syngas. However, the HGS can be environmentally beneficial due to the reduction in waste disposal in landfills. The LCOE was EUR 174–191 with a minimum at the RES capacity of 14 MW. Any change in waste disposal costs and emission allowances would cause significant changes in the LCOE. It was found that it can be beneficial to use a gasification system as a balancing source in a HGS.
Foraging Honeybees (Apis mellifera ligustica) as Biocenosis Monitors of Pollution in Areas Affected by Cement Industry Emissions
Authors: Sebastiani Bartolomeo , Mariucci Sara , Palmieri Nicola
Published: February 2023

Two areas affected by cement plant emissions, in an industrial district of Central Italy, were investigated by foraging honeybees (Apis mellifera ligustica) on the return to their hives, as an in situ biomonitor. The contamination was compared with that of a background reference area on the Central Apennine Mountains, quite far from the contamination sources. At all the sites, the bee colonies were stationary. One hundred seventy-seven compounds belonging to the class of polycyclic aromatic compounds (PACs) were positively identified by gas chromatographic and mass spectrometric techniques. For the first time, the presence of several unusual compounds on bee samples is highlighted. These include polycyclic aromatic sulfur heterocycles (PASHs), 1.55–35.63 ng/g d.w., compounds that, like polycyclic aromatic hydrocarbons (PAHs), 67.50–129.95 ng d.w., are classified as carcinogenic and/or mutagenic. In an attempt to identify the contribution of different and specific sources of these pollutants to the total pollution profile, the composition of aliphatic linear hydrocarbons was also examined.
Estimating Summer Arctic Warming Amplitude Relative to Pre-Industrial Levels Using Tree Rings
Authors: Gao Cong , Shi Chunming , Lou Yuxin , An Ran , Sun Cheng , Wu Guocan , Zhang Yuandong , Shen Miaogen , Chen Deliang
Published: February 2023

Estimating long-term trends and short-term amplitudes requires reliable temperature (Temp) observations in the pre-industrial period when few in situ observations existed in the Arctic. Tree-ring materials are most available and used to reconstruct past Arctic Temp variations. However, most previous studies incorporated materials that are insensitive to local Temp variabilities. The derived reconstruction qualities are low (indicated by low calibration R2), and the uncertainties inherent in the various detrending methodologies are unknown. To reconstruct Arctic (N60°–N90°) summer (June–August) Temp in 1850–1900 and variations over the past centuries, we screened 1116 tree-ring width and tree-ring density records and applied four detrending functions (sf-RCS, RCS, MOD, and spline). In total, 338–396 records show significant correlations (p < 0.05) with the Climate Research Unit (CRU) Temp of the corresponding grid point. These records were selected and combined into a proxy record. The achieved Arctic summer Temp reconstruction explained 45–57% of the instrumental summer Temp variance since 1950. The 2012–2021 summer Arctic warming amplitudes (1.42–1.74 °C) estimated by Temp anomaly datasets extending back to 1850 are within the range derived from our reconstructions, despite using various detrending methods. These findings could suggest the Berkeley and HadCRU5 datasets interpolating Temp from a few (6–73) meteorological stations could still represent the mean Arctic Temp variation in 1850–1900, and the updated reconstruction can be used as a reliable reference for 1550–2007 Arctic summer Temp history.
Net-Carbon Dioxide Surplus as an Environmental Indicator for Supporting Timber Markets: A Case Study in Italy
Authors: Carbone Francesco , Corona Piermaria , Hussain Majid , Barbarese Francesco
Published: February 2023

Using the Life Cycle Assessment (LCA) approach, environmental benefits in terms of CO2 stored in chestnut wood in Italy have been calculated. Using one of the methodologies proposed under the LCA umbrella, a physical and formal balance sheet of CO2 has been built. Chestnut forests (Castanea sativa Mill.) are one of the most critical forest types in Europe. They cover an area of 800,000 hectares in Italy, most of which are managed as coppices. Chestnut wood’s high-quality physical-chemical and mechanical characteristics and medium-long durability explains its widespread uses. In this case study a section of a public forest in Central Italy (Lazio Region) has been considered. In the section, during the rotation, two types of intervention were carried out: thinning at 19 years of age, and final cutting at the age of 32. A production of 416 and 93 m3ha−1 for final cutting and thinning, respectively, was recorded. The global amount of 507 m3 is the functional unit, which has stored 547,875 kgCO2. The combination of forest management and sawmill processing produces semi-finished chestnut timber products for 125 m3, which have a physical storage of 135,210 kgCO2. Using the formal balance sheet of CO2, total emissions from processing were recorded for a total of 27,766 kgCO2. At the exit of sawmill, products stored 107,444 kgCO2, which is the amount of Net-Carbon Dioxide Surplus (Net-CDS). Transportation from sawmill to market reduces the sequestered CO2 by 0.77 kgCO2/km. The Net-CDS represents a competitive advantage in the timber market. If tree species have the same physical, chemical, mechanical and price parameters, the timber consumer would prefer to buy wood with the highest Net-CDS.
Expert-Based Assessment of the Potential of Non-Wood Forest Products to Diversify Forest Bioeconomy in Six European Regions
Authors: Huber Patrick , Kurttila Mikko , Hujala Teppo , Wolfslehner Bernhard , Sanchez-Gonzalez Mariola , Pasalodos-Tato Maria , de-Miguel Sergio , Bonet José Antonio , Marques Marlene , Borges Jose G. , Enescu Cristian Mihai , Dinca Lucian , Vacik Harald
Published: February 2023

The forest-based sector plays a significant role in supporting Europe on its pathway towards a more integrated and bio-based circular economy. Beyond the supply of timber, forest ecosystems offer a wide range of products and services beneficial to human wellbeing. Non-wood forest products (NWFPs) play an integral role in provisioning forest ecosystem services and constitute a huge portfolio of species from various taxonomic kingdoms. As diverse as the resources themselves is the list of end-products that may be derived from raw non-wood materials. Multiple value-chains of NWFPs provide benefits to actors across all stages of the supply chain. Forest management has not yet directed full attention towards NWFPs, since timber production remains the main management objective, although multi-purpose management is recognised as a key principle of the sector’s sustainability paradigm. Lack of knowledge of the socio-economic relevance of NWFPs for European societies and diverse property rights frameworks increase the complexity in forest-based decision making additionally. In this study, the future potential of 38 NWFPs for diversifying the forest bioeconomy is investigated by means of multi-criteria analysis, including stakeholder interaction and expert involvement. The results for six case studies in different biogeographical zones in Europe indicate the latent opportunities NWFPs provide to forest owners who are willing to focus their management on the joint production of wood and non-wood resources as well as their value networks. This study intends to unravel perspectives for forest owners in particular, as they often represent principal decision makers in forest ecosystem management, act as main suppliers of NWFP raw materials, and thus can be understood as key stakeholders in a forest bioeconomy. Even though regional perspectives differ, due to varying socio-economic and ecological environments, there is huge potential to strengthen the economic viability of rural areas. Furthermore, sustainable co-production may foster the ecological integrity of forest ecosystems across Europe. Results show that wild mushrooms constitute the most widespread opportunity to increase additional income from forest management, but the most promising NWFPs can be found in the tree product, understorey plant and animal origin categories.
Efficient Procedure for Induction Somatic Embryogenesis in Holm Oak: Roles of Explant Type, Auxin Type, and Exposure Duration to Auxin
Authors: Martínez María Teresa , Corredoira Elena
Published: February 2023

Holm oak is the dominant tree species in the Mediterranean climate. Currently, worrisome degradation of its ecosystems has been observed, produced, among other factors, by changes in land use, extreme weather events, forest fires, climate change, and especially the increasingly frequent episodes of high tree mortality caused by “oak decline”, which has brought with it a social concern that transcends the productive interest. Breeding and conservation programs for this species are necessary to ensure the prevalence of these ecosystems for future generations. Biotechnological tools such as somatic embryogenesis (SE) have great potential value for tree improvement and have been shown to be highly efficient in the propagation and conservation of woody species. One challenge to this approach is that SE induction in holm oak has not yet been optimized. Here, we present a new reproducible procedure to induce SE in holm oak; we evaluated the responsiveness of different initial explants exposed to different types, concentrations, and durations of auxin. SE rates were significantly improved (37%) by culturing nodal segments for two weeks in induction medium. In addition, a significant auxin–genotype interaction was observed.
Impacts of Different Reforestation Methods on Fungal Community and Nutrient Content in an Ex-Tea Plantation
Authors: Liang Anjie , Wen Xinyi , Yu Wenjing , Su Shunde , Lin Yongming , Fan Hailan , Su Jun , Wu Chengzhen
Published: February 2023

Long-term monocultures of tea and the excessive use of chemical fertilizer lead to the degradation of soil quality. Improving the soil quality of ex-tea plantations through vegetation restoration is an important task. However, the changes in soil nutrients, fungal communities, and the effects of microorganisms on soil nutrients after reforestation remain unclear. Therefore, in this study, we aimed to explore the effects of Pinus and Chinese fir on soil nutrients and fungal communities in ex-tea plantation areas that were subjected to the reforestation modes of pure forest and mixed forest by measuring soil chemical properties and ITS rRNA gene sequences. The results showed that (1) after reforestation, the relative normalized difference vegetation index (NDVI) of the Mixed forest, Mixed Pine and Mixed Fir areas increased (p < 0.05) compared to that of pure forest; (2) the soil organic carbon (SOC), total nitrogen (TN), and N:P ratios of the mixed forest increased by an average of 54%, 90%, and 299% (p < 0.05) compared to pure forest, whereas the total phosphorus (TP) and available potassium (AK) decreased by an average of 39% and 89% (p < 0.05); and (3) there was no significant difference in the diversity of the fungal communities of the pure and mixed forests, but the fungal phyla Mucoromycota, Glomeromycota, and Rozellomycota were significantly different in the pure and mixed forests. This differing microbial composition led to a significant increase (p < 0.05) in symbiotrophs (ecotomycorhizal, ericoid mycorhizal) in the mixed forest, which was negatively correlated with the soil TP and positively correlated with the TN and the N:P ratio. In addition, there was also a significant decrease (p < 0.05) in complex nutrient types (ectomycorrhizal-fungal parasite-plant saprotroph-wood saprotroph), which were negatively correlated with the SOC and TN, and arbuscular mycorrhizas, which were positively correlated with the TP. Our results show that the chemical properties of soils and the structure of the fungal communities changed significantly due to the reforestation of Chinese fir and Pinus, and the mixed forest mode of reforestation was more conducive to improving the soil quality; therefore, a mixed forest of Chinese fir and Pinus can be used to improve degraded soils in ex-tea planting areas.
The Potential of Using Istrian Albumin Cheese Whey in the Production of Whey Distillate
Authors: Bendelja Ljoljić Darija , Kalit Samir , Kazalac Josipa , Dolenčić Špehar Iva , Mihaljević Žulj Marin , Maslov Bandić Luna , Tudor Kalit Milna
Published: February 2023

In addition to the production of albumin cheese, biogas and whey powder, whey has also been used as a raw material for the production of alcoholic beverages. The aim of this research was to investigate the potential of using Istrian albumin cheese whey in the production of whey distillate. Three batches of Istrian albumin cheese were produced in a small-scale cheese plant. The remaining whey after the production of albumin cheese was fermented using Kluyveromyces marxianus subsp. marxianus for 5 days and then distilled. In the whey samples before and after fermentation, the content of total solids, protein, milk fat and lactose was determined. The ethanol content and the composition of volatile compounds in the distillate was determined. The content of all components of the chemical composition of whey, except for the content of fat, decreased during fermentation. In the whey distillate with an alcohol content of 35%, the following compounds were determined: acetaldehyde, methanol, n-propanol, i-butanol, n-butanol, 3-methyl-1-ol, 2-methylbutan-1-ol and ethyl-lactate. The distillate obtained confirmed the potential of using whey for the production of spirits. However, further research is needed due to the potential presence of the harmful effects of certain volatile compounds on human health.
Transcriptional Response of Multi-Stress-Tolerant Saccharomyces cerevisiae to Sequential Stresses
Authors: Costa Ane Catarine Tosi , Russo Mariano , Fernandes A. Alberto R. , Broach James R. , Fernandes Patricia M. B.
Published: February 2023

During the fermentation process, yeast cells face different stresses, and their survival and fermentation efficiency depend on their adaptation to these challenging conditions. Yeast cells must tolerate not only a single stress but also multiple simultaneous and sequential stresses. However, the adaptation and cellular response when cells are sequentially stressed are not completely understood. To explore this, we exposed a multi-stress-tolerant strain (BT0510) to different consecutive stresses to globally explore a common response, focusing on the genes induced in both stresses. Gene Ontology, pathway analyses, and common transcription factor motifs identified many processes linked to this common response. A metabolic shift to the pentose phosphate pathway, peroxisome activity, and the oxidative stress response were some of the processes found. The SYM1, STF2, and HSP genes and the transcription factors Adr1 and Usv1 may play a role in this response. This study presents a global view of the transcriptome of a multi-resistance yeast and provides new insights into the response to sequential stresses. The identified response genes can indicate future directions for the genetic engineering of yeast strains, which could improve many fermentation processes, such as those used for bioethanol production and beverages.
Petroleum Hydrocarbon Catabolic Pathways as Targets for Metabolic Engineering Strategies for Enhanced Bioremediation of Crude-Oil-Contaminated Environments
Authors: Das Nandita , Das Ankita , Das Sandeep , Bhatawadekar Vasudha , Pandey Prisha , Choure Kamlesh , Damare Samir , Pandey Piyush
Published: February 2023

Anthropogenic activities and industrial effluents are the major sources of petroleum hydrocarbon contamination in different environments. Microbe-based remediation techniques are known to be effective, inexpensive, and environmentally safe. In this review, the metabolic-target-specific pathway engineering processes used for improving the bioremediation of hydrocarbon-contaminated environments have been described. The microbiomes are characterised using environmental genomics approaches that can provide a means to determine the unique structural, functional, and metabolic pathways used by the microbial community for the degradation of contaminants. The bacterial metabolism of aromatic hydrocarbons has been explained via peripheral pathways by the catabolic actions of enzymes, such as dehydrogenases, hydrolases, oxygenases, and isomerases. We proposed that by using microbiome engineering techniques, specific pathways in an environment can be detected and manipulated as targets. Using the combination of metabolic engineering with synthetic biology, systemic biology, and evolutionary engineering approaches, highly efficient microbial strains may be utilised to facilitate the target-dependent bioprocessing and degradation of petroleum hydrocarbons. Moreover, the use of CRISPR-cas and genetic engineering methods for editing metabolic genes and modifying degradation pathways leads to the selection of recombinants that have improved degradation abilities. The idea of growing metabolically engineered microbial communities, which play a crucial role in breaking down a range of pollutants, has also been explained. However, the limitations of the in-situ implementation of genetically modified organisms pose a challenge that needs to be addressed in future research.
Research Trends of the Internet of Things in Relation to Business Model Innovation: Results from Co-Word and Content Analyses
Authors: Kulakli Atik , Arikan Cenk Lacin
Published: February 2023

In the era of the Internet of Things, innovative business model initiatives continue to deepen, and the trend of search domains continues to expand. This paper aims to scientifically analyze research trends of the Internet of Things in relation to Business Model Innovation through bibliometric studies. The data were collected using the Clarivate Web of Science (WoS) Core Collection (SSCI and SCI indexed) from 2005 to 2022 (November). However, the publications for the research domains started in 2015. The results show that scientific publications on the Internet of Things in relation to Business Model Innovation have increased gradually since 2019. The WoS database is utilized for analyses because it contains journals and conference proceedings deemed more relevant by the academic domain and highly reputable sources for bibliometric studies. The VOS viewer, R Language, and Microsoft Excel were also used to analyze and complete the study. Bibliometric and scientometric analyses were conducted to identify publication patterns, text analysis, most important keywords (co-word, word cloud, and co-occurrence), trends for the topicality, and content clustering for the publication periods. The visualization of the research trends of the Internet of Things in relation to Business Model Innovation resulted in four co-occurrence clusters leading to some of the topic areas mentioned as follows: (1) The Internet of Things, (2) Business model innovation, (3) Technology infrastructure, and (4) Digital transformation and capabilities. The results of this study will assist academics in identifying worldwide research trends related to the Internet of Things and Business Model Innovation as well as recommending future research areas.