bluuebunny/crossref_metadata_2025 huggingface dataset:
From Feasting to Fasting: The Arginine Pathway as a Metabolic Switch in Nitrogen-Deprived Chlamydomonas reinhardtii
Authors: Monteiro Lucca de Filipe Rebocho , Giraldi Laís Albuquerque , Winck Flavia Vischi
Published: May 2023

The metabolism of the model microalgae Chlamydomonas reinhardtii under nitrogen deprivation is of special interest due to its resulting increment of triacylglycerols (TAGs), that can be applied in biotechnological applications. However, this same condition impairs cell growth, which may limit the microalgae’s large applications. Several studies have identified significant physiological and molecular changes that occur during the transition from an abundant to a low or absent nitrogen supply, explaining in detail the differences in the proteome, metabolome and transcriptome of the cells that may be responsible for and responsive to this condition. However, there are still some intriguing questions that reside in the core of the regulation of these cellular responses that make this process even more interesting and complex. In this scenario, we reviewed the main metabolic pathways that are involved in the response, mining and exploring, through a reanalysis of omics data from previously published datasets, the commonalities among the responses and unraveling unexplained or non-explored mechanisms of the possible regulatory aspects of the response. Proteomics, metabolomics and transcriptomics data were reanalysed using a common strategy, and an in silico gene promoter motif analysis was performed. Together, these results identified and suggested a strong association between the metabolism of amino acids, especially arginine, glutamate and ornithine pathways to the production of TAGs, via the de novo synthesis of lipids. Furthermore, our analysis and data mining indicate that signalling cascades orchestrated with the indirect participation of phosphorylation, nitrosylation and peroxidation events may be essential to the process. The amino acid pathways and the amount of arginine and ornithine available in the cells, at least transiently during nitrogen deprivation, may be in the core of the post-transcriptional, metabolic regulation of this complex phenomenon. Their further exploration is important to the discovery of novel advances in the understanding of microalgae lipids’ production.
Pharmacological Significance of Heme Oxygenase 1 in Prostate Cancer
Authors: Ben-Eltriki Mohamed , Gayle Erysa J. , Walker Noah , Deb Subrata
Published: May 2023

Heme oxygenase 1 (HO-1) is a detoxifying antioxidant microsomal enzyme that regulates inflammation, apoptosis, cell proliferation, and angiogenesis in prostate cancer (PCa). This makes HO-1 a promising target for therapeutic prevention and treatment due to its anti-inflammatory properties and ability to control redox homeostasis. Clinical evidence highlights the possible correlation between HO-1 expression and PCa growth, aggressiveness, metastasized tumors, resistance to therapy, and poor clinical outcomes. Interestingly, studies have reported anticancer benefits mediated by both HO-1 induction and inhibition in PCa models. Contrasting evidence exists on the role of HO-1 in PCa progression and possible treatment targets. Herein, we provide an overview of available evidence on the clinical significance of HO-1 signaling in PCa. It appears that the beneficial effects of HO-1 induction or inhibition are dependent on whether it is a normal versus malignant cell as well as the intensity (major vs. minor) of the increase in HO-1 enzymatic activity. The current literature evidence indicates that HO-1 has dual effects in PCa. The amount of cellular iron and reactive oxygen species (ROS) can determine the role of HO-1 in PCa. A major increase in ROS enforces HO-1 to a protective role. HO-1 overexpression may provide cryoprotection to normal cells against oxidative stress via suppressing the expression of proinflammatory genes, and thus offer therapeutic prevention. In contrast, a moderate increase in ROS can lead to the perpetrator role of HO-1, which is associated with PCa progression and metastasis. HO-1 inhibition by xenobiotics in DNA-damaged cells tilts the balance to promote apoptosis and inhibit PCa proliferation and metastasis. Overall, the totality of the evidence revealed that HO-1 may play a dual role in the therapeutic prevention and treatment of PCa.
Influence of Auditory Integrative Training on Casein Kinase 2 and Its Impact on Behavioral and Social Interaction in Children with Autism Spectrum Disorder
Authors: Al-Ayadhi Laila , Bhat Ramesa Shafi , Alghamdi Farah Ali , Alhadlaq Abdulmalik S. , El-Ansary Afaf
Published: May 2023

Considerable disturbances in post-translational protein phosphorylation have recently been discovered in multiple neurological disorders. Casein kinase-2 (CK2) is a tetrameric Ser/Thr protein kinase that phosphorylates a large number of substrates and contributes in several cellular physiological and pathological processes. CK2 is highly expressed in the mammalian brain and catalyzes the phosphorylation of a large number of substrates that are crucial in neuronal or glial homeostasis and inflammatory signaling processes across synapses. In this study, we investigated the impact of auditory integration therapy (AIT) for the treatment of sensory processing abnormalities in autism on plasma CK2 levels. A total of 25 ASD children, aged between 5 and 12 years, were enrolled and participated in the present research study. AIT was performed for two weeks, for a period of 30 min, twice a day, with a 3 h interval between sessions. Before and after AIT, the Childhood Autism Rating Scale (CARS), Social Responsiveness Scale (SRS), and Short Sensory Profile (SSP) scores were calculated, and plasma CK2 levels were assayed using an ELISA test. The CARS and SRS indices of autism severity improved as a result of AIT, which could be related to the decreased level of plasma CK2. However, the mean value of the SSP scores was not significantly increased after AIT. The relationship between CK2 downregulation and glutamate excitotoxicity, neuro-inflammation, and leaky gut, as etiological mechanisms in ASD, was proposed and discussed. Further research, conducted on a larger scale and with a longer study duration, are required to assess whether the cognitive improvement in ASD children after AIT is related to the downregulation of CK2.
Blockchain-Based Electronic Voting: A Secure and Transparent Solution
Authors: Pereira Bruno Miguel Batista , Torres José Manuel , Sobral Pedro Miguel , Moreira Rui Silva , Soares Christophe Pinto de Almeida , Pereira Ivo
Published: May 2023

Since its appearance in 2008, blockchain technology has found multiple uses in fields such as banking, supply chain management, and healthcare. One of the most intriguing uses of blockchain is in voting systems, where the technology can overcome the security and transparency concerns that plague traditional voting systems. This paper provides a thorough examination of the implementation of a blockchain-based voting system. The proposed system employs cryptographic methods to protect voters’ privacy and anonymity while ensuring the verifiability and integrity of election results. Digital signatures, homomorphic encryption (He), zero-knowledge proofs (ZKPs), and the Byzantine fault-tolerant consensus method underpin the system. A review of the literature on the use of blockchain technology for voting systems supports the analysis and the technical and logistical constraints connected with implementing the suggested system. The study suggests solutions to problems such as managing voter identification and authentication, ensuring accessibility for all voters, and dealing with network latency and scalability. The suggested blockchain-based voting system can provide a safe and transparent platform for casting and counting votes, ensuring election results’ privacy, anonymity, and verifiability. The implementation of blockchain technology can overcome traditional voting systems’ security and transparency shortcomings while also delivering a high level of integrity and traceability.
Novel Mixed-Phase α/γ-Fe2O3 Micro-Flower Assembled with Nanosheets for Enhancing Acetone Detection
Authors: Tian Ruonan , Tan Huai , Chen Gang , Guan Hongtao , Dong Chengjun , Yin Zongyou
Published: May 2023

Although individual γ-Fe2O3 and α-Fe2O3 have been widely fabricated for gas sensors, their mixed phase of α/γ-Fe2O3 might deliver excellent sensing properties. In this study, a facile solvothermal method was used to fabricate Fe-alkoxide. After thermal treatment, it was converted into γ-Fe2O3, α-Fe2O3 and their mixed-phase α/γ-Fe2O3 with a nanosheets-assembled flower-like structure. We studied the influence of calcination temperature on the phase and sensing properties on acetone detection. The α/γ-Fe2O3 which annealed at 400 °C included 18% α-Fe2O3 and it exhibited excellent sensing performance towards acetone compared to that of γ-Fe2O3 and α-Fe2O3. It showed a response of 353 to acetone with a concentration of 200 ppm, and a low limit of detection of 0.5 ppm at 160 °C. In addition, the change in responses with acetone concentration from 50 to 200 ppm shows a good linear relationship. Moreover, this material has good reproducibility and selectivity as well as a fast response time of 22 s and recovery time of 14 s to 200 ppm. Therefore, our mixed phase of α/γ-Fe2O3 possesses great prospects for acetone detection.
Si-Doped Nitrogenated Holey Graphene (C2N) as a Promising Gas Sensor for O-Containing Volatile Organic Compounds (VOCs) and Ammonia
Authors: Liu Yang , Ye Chenxiao , Zhao Hengxin , Lin Kexin , Cao Xinrui , Ai Yuejie
Published: May 2023

Two-dimensional (2D) crystalline materials have been regarded as promising sensor materials due to their large specific surface area, high sensitivity, and low cost. In the present work, based on the density functional theory (DFT) method, the sensor performance of novel silicon (Si)-doped nitrogenated holey graphene (SiC2N) toward five typical VOCs (HCHO, CH3OH, C3H6O, C6H6, and C2HCl3) and ammonia were systematically investigated. The results demonstrated that Si doping could effectively decrease the band gap of C2N and simultaneously provide active sites for gas adsorption. Through comprehensive analyses of adsorption energies and electronic properties, the SiC2N was found to exhibit high selectivity for O-containing VOCs (HCHO, CH3OH, and C3H6O) and NH3 via a covalent bond. Moreover, after the HCHO, CH3OH, C3H6O, and NH3 adsorption, the band gap of SiC2N greatly decreases from 1.07 eV to 0.29, 0.13, 0.25, and 0.12 eV, respectively, which indicated the enhancement the conductivity and enabled the SiC2N to be a highly sensitive resistive-type sensor. In addition, the SiC2N possesses a short recovery time. For instance, the recovery time of HCHO desorbed from SiC2N is 29.2 s at room temperature. Our work anticipates a wide range of potential applications of Si-doped C2N for the detection of toxic VOCs and ammonia, and supplies a valuable reference for the development of C2N-based gas sensors.
Expanding Actinomycetota Diversity in the TBRC Culture Collection through Metabarcoding and Simulated In Situ Cultivation of Thailand’s Mekong River Microbiota
Authors: Kitikhun Supattra , Siriarchawattana Paopit , Chunhametha Suwanee , Suriyachadkun Chanwit , Rattanawaree Pattaraporn , Phithakrotchanakoon Chitwadee , Harnpicharnchai Piyanun , Eurwilaichitr Lily , Ingsriswang Supawadee
Published: May 2023

Culture-independent and culture-dependent approaches were employed to investigate the taxonomic diversity and biosynthetic gene cluster potential of Actinomycetota in the Mekong River. Through 16S rRNA gene metabarcoding, 21,103 OTUs were revealed to represent 190 genera and at least 595 species of Actinomycetota, including putatively novel taxa. Conventional and in situ cultivation (IC) methods provided 75 Actinomycetota isolates representing 72 species from 21 genera. Of these, 45 species in 4 genera were new to the Thailand Bioresource Research Center (TBRC), a collection of 20,079 Actinomycetota strains from 660 species. Applying both culture-independent and culture-dependent approaches to the same sample revealed greater diversity among the Actinomycetota in the Mekong River than one approach alone.
Molecular Phylogeny of the Genus Paracanthonchus (Nematoda: Chromadorida) with Description of P. yeongjongensis sp. nov. from Korea
Authors: Kim Hyeonggeun , Lee Wonchoel , Jeong Raehyuk
Published: May 2023

During an investigation of the intertidal zone at Yeongjongdo Island, Incheon, Korea, a new species belonging to the genus Paracanthonchus was found and is reported. Paracanthonchus Mikoletzky, 1924 is the largest genus within the family Cyatholaimidae, and the species identification of this genus has been difficult mainly due to overlapping characteristics and a lack of genus/species-defining apomorphic characters. The new species is characterized by the buccal cavity, armed with one large dorsal tooth and two subventral teeth, the presence of lateral differentiation, seventy-six tubular precloacal supplements, and proximally paired gubernaculum. Alongside the description, we are updating the review of the genus by providing an up-to-date list of valid species, as well as a comprehensive tabular key to the genus with measurements of species-discerning characteristics. We also provide partial sequences of mtCOI, 18S and 28S rRNA to verify the new species belongs to the genus Paracanthonchus and to discuss the phylogeny of the family Cyatholaimidae as well as the genus Paracanthonchus. Our phylogeny agrees with previous findings that, while Cyatholaimidae forms a monophyletic clade, many genera within, including Paracanthonchus, are paraphyletic. For now, it appears that subfamilies are not well-represented by molecular means, and much more molecular data along with species defining morphological traits will have to be accumulated to meaningfully organize this family and the genus within.
Acanthocephalan Diversity and Host Associations Revealed from a Large-Scale Biodiversity Survey
Authors: Bennett Jerusha , Poulin Robert , Presswell Bronwen
Published: May 2023

Acanthocephalans constitute a relatively small phylum of dioecious helminths that infect invertebrate intermediate and vertebrate paratenic and definitive hosts. Like most parasites, acanthocephalans are usually overlooked in biodiversity studies, although they can have significant impacts on their host’s health and the structure of surrounding communities. In this study, we present morphological and molecular data from an extensive biodiversity survey of acanthocephalans infecting a range of marine animals in a coastal marine ecosystem in New Zealand. We recovered 13 acanthocephalan species infecting 32 of the 168 free-living animal species investigated, 1 of which is a new geographic record for New Zealand (Gorgorhynchoides queenslandensis), 9 of which constitute new host records, and at least 2 that are species new to science. The data presented here provide a baseline dataset to which future assessments of changes in diversity and distribution of acanthocephalans can be compared.
Population Status of the Globally Threatened Long-Tailed Duck Clangula hyemalis in the Northeast European Tundra
Authors: Mineev Oleg , Mineev Yurij , Kochanov Sergey , Novakovskiy Alexander
Published: May 2023

Arctic Russia is home to more than 90% of all Long-tailed Ducks in the Clangula hyemalis species from the Western Siberia/Northern Europe population. The breeding population in European Russia was estimated to be about 5 million birds in the 1960s, while today, estimates have declined to 1 million birds. Up until now, the main reasons for the overall population decline of the Long-tailed Duck were related to wintering conditions in the Baltic Sea. Our data indicate that the loss or deterioration of key breeding habitats in the Arctic regions of Russia is one important factor influencing the rapid population decline. Many key breeding habitats of the Long-tailed Duck were completely lost in the Bolshezemelskaya tundra, as this area was transformed into major oil and gas extraction sites. The transformation of these sites increased the disturbance and oil pollution of adjacent habitats, leading to the direct loss of certain key nesting sites and a marked and rapid decline of the breeding population of the Long-tailed Duck in the Bolshezemelskaya tundra. Oil-spills during transportation by sea may also be an important factor of decline in the Long-tailed Duck population. Meanwhile, in the Malozemelskaya tundra, which did not experience oil and gas development, the breeding population over the last decades remained stable. Urgent establishment of new protections in key breeding areas in Arctic Russia, sustainable population management, and new research programs are necessary for the conservation and enhancement of this globally threatened species.
Anomalous Diffusion of Polyelectrolyte Segments on Supported Charged Lipid Bilayers
Authors: Yu Shi , Zhao Jianqiao , Chu Ruizhi , Li Xiao , Wu Guoguang , Meng Xianliang
Published: May 2023

This work provides mesoscale models for the anomalous diffusion of a polymer chain on a heterogeneous surface with rearranging randomly distributed adsorption sites. Both the “bead-spring” model and oxDNA model were simulated on supported lipid bilayer membranes with various molar fractions of charged lipids, using Brownian dynamics method. Our simulation results demonstrate that “bead-spring” chains exhibit sub-diffusion on charged lipid bilayers which agrees with previous experimental observations for short-time dynamics of DNA segments on membranes. In addition, the non-Gaussian diffusive behaviors of DNA segments have not been observed in our simulations. However, a simulated 17 base pairs double stranded DNA, using oxDNA model, performs normal diffusion on supported cationic lipid bilayers. Due to the number of positively charged lipids attracted by short DNA is small, the energy landscape that the short DNA experiences during diffusion is not as heterogeneous as that experienced by long DNA chains, which results in normal diffusion rather than sub-diffusion for short DNA.
Uncertainty Management in Assessment of FMEA Expert Based on Negation Information and Belief Entropy
Authors: Wu Lei , Tang Yongchuan , Zhang Liuyuan , Huang Yubo
Published: May 2023

The failure mode and effects analysis (FMEA) is a commonly adopted approach in engineering failure analysis, wherein the risk priority number (RPN) is utilized to rank failure modes. However, assessments made by FMEA experts are full of uncertainty. To deal with this issue, we propose a new uncertainty management approach for the assessments given by experts based on negation information and belief entropy in the Dempster–Shafer evidence theory framework. First, the assessments of FMEA experts are modeled as basic probability assignments (BPA) in evidence theory. Next, the negation of BPA is calculated to extract more valuable information from a new perspective of uncertain information. Then, by utilizing the belief entropy, the degree of uncertainty of the negation information is measured to represent the uncertainty of different risk factors in the RPN. Finally, the new RPN value of each failure mode is calculated for the ranking of each FMEA item in risk analysis. The rationality and effectiveness of the proposed method is verified through its application in a risk analysis conducted for an aircraft turbine rotor blade.
A Novel Power Measurement Method Using Lock-In Amplifiers with a Frequency-Locked Loop
Authors: Rehman Abdur , Cho Kangcheoul , Choi Woojin
Published: May 2023

The extensive use of renewable energy systems with grid-connected inverters (GCIs) causes harmonic injection. Similarly, the imbalance in energy demand and supply causes frequency fluctuations. As a result of the increased harmonics and frequency fluctuations, the accuracy of power measurement using conventional methods continues to decline. Precision in power measurement is an essential factor for the billing and management of power supply and demand. Moreover, it is challenging to build a supply plan for the power demand and to manage the billing for the power consumption. To solve these problems, this paper proposes a novel method based on Lock-in Amplifier (LIA) and Lock-in Amplifier Frequency-Locked Loop (LIA-FLL) to measure the power with high precision and accuracy. The proposed method first tracks the variations in the input signal frequency using LIA-FLL and generates the updated reference signals for LIA. After that, the LIA is used to extract the accurate amplitude of each frequency component. The proposed method results in accurate and precise measurement, even with harmonics and frequency fluctuations. The validity of the proposed method is verified by comparing the power measurement results with the classical method, FFT, and ZERA COM3003 (a commercially available power measurement reference instrument).
Intelligent Geomagnetic Indoor Positioning System
Authors: Kuo Yen-Hui , Wu Eric Hsiao-Kuang
Published: May 2023

In the past, several firefighters have died in disaster relief operations. Although the firefighters were fully equipped, the scene of the disaster was smoky and disorienting, making the firefighters unable to identify their location. The commander wanted to direct the firefighters outside but could not confirm the correct location of the firefighters, causing delays in rescue. GPS cannot support indoor positioning or preset indoor positioning facilities at the moment of fire extinguishing. However, geomagnetism is everywhere, and it can be used to identify one’s location. Unfortunately, due to the uncertainty of the magnetic field strength, indoor geomagnetism is affected by the building environment, and the existing magnetic positioning methods have difficulty obtaining a location. To solve this problem, we propose a new incremental indoor localization scheme based on the difference in geomagnetic intensity. The proposed method achieves indoor localization in 2D environments successfully. The novelty of our geomagnetic indoor positioning system is that it can perform indoor positioning without adding any indoor positioning facilities, and the accuracy can reach 0.8~1.5 m. This article aims to verify that the geomagnetic turbulence filtering algorithm can filter out abnormal geomagnetic intensity, that the incremental algorithm can estimate the position of human motion, and that geomagnetism can be used for indoor positioning without any preset infrastructure. The contribution of this paper is that we have developed a practical system that can be used without any infrastructure and can be used for indoor positioning with meter-level accuracy. The geomagnetic indoor positioning system can be integrated with a wireless network and applied to disaster relief.
An Enhanced Deep Learning Model for Obstacle and Traffic Light Detection Based on YOLOv5
Authors: Li Zhenwei , Zhang Wei , Yang Xiaoli
Published: May 2023

Timely detection of dynamic and static obstacles and accurate identification of signal lights using image processing techniques is one of the key technologies for guidance robots and is a necessity to assist blind people with safe travel. Due to the complexity of real-time road conditions, current obstacle and traffic light detection methods generally suffer from missed detection and false detection. In this paper, an improved deep learning model based on YOLOv5 is proposed to address the above problems and to achieve more accurate and faster recognition of different obstacles and traffic lights that the blind may encounter. In this model, a coordinate attention layer is added to the backbone network of YOLOv5 to improve its ability to extract effective features. Then, the feature pyramid network in YOLOv5 is replaced with a weighted bidirectional feature pyramid structure to fuse the extracted feature maps of different sizes and obtain more feature information. Finally, a SIoU loss function is introduced to increase the angle calculation of the frames. The proposed model’s detection performance for pedestrians, vehicles, and traffic lights under different conditions is tested and evaluated using the BDD100K dataset. The results show that the improved model can achieve higher mean average precision and better detection ability, especially for small targets.
Industrial Informatics: Emerging Trends and Applications in the Era of Big Data and AI
Authors: Pejić Bach Mirjana , Ivec Arian , Hrman Danijela
Published: May 2023

Industrial informatics is a rapidly developing scientific field that deals with the knowledge-based automation of industrial design and manufacturing processes. In the last decade, industrial informatics has been strongly influenced by the rapid rise of data-based technologies such as Data Science, Big Data, and artificial intelligence. The goal of this paper is to provide a literature review of academic research analyzing the extensive spectrum of industrial informatics. Articles indexed in Scopus with the term “Industrial Informatics” in the title, abstract, or keywords were extracted since the term emerged in the 1990s, over a period of 29 years. The main journals, conferences, authors and countries were studied using bibliometric analysis. Text mining using VosViewer was used to extract the thematic groups of research related to industrial informatics, which are as follows: (i) Internet of Things, (ii) machine learning, (iii) engineering education, (iv) cyber–physical systems, and (v) embedded systems. We also found that China, Germany, and Brazil dominate research in industrial computing. The results showed that research in industrial informatics is related to the emergence of new methods and tools, and is nowadays shifting towards the application of intelligent methods such as machine learning and Big Data.
Why Does the PV Solar Power Plant Operate Ineffectively?
Authors: Alhmoud Lina
Published: May 2023

Quality, reliability, and durability are the key features of photovoltaic (PV) solar system design, production, and operation. They are considered when manufacturing every cell and designing the entire system. Achieving these key features ensures that the PV solar system performs satisfactorily and offers years of trouble-free operation, even in adverse conditions. In each cell, the quality of the raw material should meet the quality standards. The fulfillment of the quality management system requires every part that goes into the PV solar system to undergo extensive testing in laboratories and environments to ensure it meets expectations. Hence, every MWh of electricity generated by the PV solar system is counted, the losses should be examined, and the PV system’s returns should be maximized. There are many types of losses in the PV solar system; these losses are identified and quantified based on knowledge and experience. They can be classified into two major blocks: optical and electrical losses. The optical losses include, but are not limited to, partial shading losses, far shading losses, near shading losses, incident angle modifier (IAM) losses, soiling losses, potential induced degradation (PID) losses, temperature losses, light-induced degradation (LID) losses, PV yearly degradation losses, array mismatch losses, and module quality losses. In addition, there are cable losses inside the PV solar power system, inverter losses, transformer losses, and transmission line losses. Thus, this work reviews the losses in the PV solar system in general and the 103 MWp grid-tied Al Quweira PV power plant/Aqaba, mainly using PVsyst software. The annual performance ratio (PR) is 79.5%, and the efficiency (η) under standard test conditions (STC) is 16.49%. The normalized production is 4.64 kWh/kWp/day, the array loss is 1.69 kWh/kWp/day, and the system loss is 0.18 kWh/kWp/day. Understanding factors that impact the PV system production losses is the key to obtaining an accurate production estimation. It enhances the annual energy and yield generated from the power plant. This review benefits investors, energy professionals, manufacturers, installers, and project developers by allowing them to maximize energy generation from PV solar systems and increase the number of solar irradiation incidents on PV modules.
Simulation Model for Operational Planning of City Cargo Transportation by Trams in Conditions of Stochastic Demand
Authors: Merkisz-Guranowska Agnieszka , Shramenko Natalya , Kiciński Marcin , Shramenko Vladyslav
Published: May 2023

A city’s transport and distribution system requires the effective organization of urban freight deliveries that take into consideration the economic, ecological, and social impact. Implementation of the concept of green logistics necessitates the use of environmentally friendly and energy-efficient modes of transport, which consider the existing infrastructure and the possibility of its development. The aim of this work was to facilitate the transfer to rail transport, using the existing tramway infrastructure, those shipments which previously entered the city center by road. The paper proposes a simulation model for small consignments delivered by freight trams in the city of Poznań, Poland. Operational planning is carried out in conditions of uncertainty and risk. The uncertainty is due to the stochastic nature of the demand for products. The risk is characterized by the probability that, due to technological and technical limitations, a given customer will not be fully served from the distribution center. The authors provide a decision support tool for evaluating the possible locations of tram depots and for route planning. The assignment of the routes, with the criterion of minimizing the costs for the carrier, and with a limitation on tram operating time, is based on the use of genetic algorithms, which makes it possible to obtain a solution that is close to optimal, within a reasonable time period. As a result of a series of simulation experiments and statistical data processing, the distribution laws and expected average values of the technological parameters were determined for the functioning of the city transport and distribution system under conditions of uncertainty and risk.
A Mycorrhizal Model for Transactive Solar Energy Markets with Battery Storage
Authors: Gould Zachary Michael Isaac , Mohanty Vikram , Reichard Georg , Saad Walid , Shealy Tripp , Day Susan
Published: May 2023

Distributed market structures for local, transactive energy trading can be modeled with ecological systems, such as mycorrhizal networks, which have evolved to facilitate interplant carbon exchange in forest ecosystems. However, the complexity of these ecological systems can make it challenging to understand the effect that adopting these models could have on distributed energy systems and the magnitude of associated performance parameters. We therefore simplified and implemented a previously developed blueprint for mycorrhizal energy market models to isolate the effect of the mycorrhizal intervention in allowing buildings to redistribute portions of energy assets on competing local, decentralized marketplaces. Results indicate that the applied mycorrhizal intervention only minimally affects market and building performance indicators—increasing market self-consumption, decreasing market self-sufficiency, and decreasing building weekly savings across all seasonal (winter, fall, summer) and typological (residential, mixed-use) cases when compared to a fixed, retail feed-in-tariff market structure. The work concludes with a discussion of opportunities for further expansion of the proposed mycorrhizal market framework through reinforcement learning as well as limitations and policy recommendations considering emerging aggregated distributed energy resource (DER) access to wholesale energy markets.
Analysis of the National Annual Emission of Pollutants from Road Transport in Poland in the Years 1990–2020
Authors: Szczepański Krystian , Bebkiewicz Katarzyna , Chłopek Zdzisław , Sar Hubert , Zakrzewska Dagna
Published: May 2023

This article presents the official results of the inventory of pollutants in Poland from road transport, developed in the National Centre for Emissions Management of the Institute of Environmental Protection–National Research Institute in Warsaw as part of the reporting of national pollutant emissions. Considerations concern pollutants harmful to the health of living organisms. Source information is available in official reports. The national emission of pollutants covers the years of balancing 1990–2020. The aim of the work is to assess the dynamic properties of the processes of national emission of pollutants. The list of substances whose emission is examined in the article is consistent with the list of substances in accordance with the European Monitoring and Evaluation Programme/European Environment Agency (EMEP/EEA) procedure. These are pollutants harmful to the health of living organisms. The inventory of pollutants contributing to the intensification of the greenhouse effect in the atmosphere is carried out as part of separate reports. The energy consumption of road vehicles was presented. The ratio of national annual emissions of pollutants in 2020 and in 1990 is the lowest for sulfur oxides and lead, followed by non-methane volatile organic compounds and carbon monoxide. The analysis of the national pollutant emission concerned the energy emission factor, which characterizes the ecological level of the accumulated category of road vehicles. This is an original element, not seen before in world literature and official reports of research institutes. An unambiguous trend of decreasing the energy emission factor of pollutants, apart from ammonia, was found, which is related to the use of catalytic flue gas purification systems. This is confirmed by the analysis of the ratio of the energy emission factor in 2020 and 1990, as well as the relative derivative of the energy emission factor in relation to the national annual emission of pollutants.
A Predictive Fuzzy Logic Model for Forecasting Electricity Day-Ahead Market Prices for Scheduling Industrial Applications
Authors: Plakas Konstantinos , Karampinis Ioannis , Alefragis Panayiotis , Birbas Alexios , Birbas Michael , Papalexopoulos Alex
Published: May 2023

Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this process has also become important for industrial companies, as their production schedules are greatly impacted by energy costs. Although various approaches have been tested with varying degrees of success, this study focuses on predicting day-ahead market (DAM) prices in different European markets and how this directly affects the optimal production scheduling for various industrial loads. We propose a fuzzy-based architecture that incorporates the results of two forecasting algorithms; a random forest (RF) and a long short-term memory (LSTM). To enhance the accuracy of the proposed model for a specific country, electricity market data from neighboring countries are also included. The developed DAM price forecaster can then be utilized by energy-intensive industries to optimize their production processes to reduce energy costs and improve energy-efficiency. Specifically, the tool is important for industries with multi-site production facilities in neighboring countries, which could reschedule the production processes depending on the forecasted electricity market price.
Techno-Economic Assessment of Residential Heat Pump Integrated with Thermal Energy Storage
Authors: Sultan Sara , Hirschey Jason , Kumar Navin , Cui Borui , Liu Xiaobing , LaClair Tim J. , Gluesenkamp Kyle R.
Published: May 2023

Phase change material (PCM)-based thermal energy storage (TES) can provide energy and cost savings and peak demand reduction benefits for grid-interactive residential buildings. Researchers established that these benefits vary greatly depending on the PCM phase change temperature (PCT), total TES storage capacity, system configuration and location and climate of the building. In this study, preliminary techno-economic performance is reported for a novel heat pump (HP)-integrated TES system using an idealized approach. A simplified HP-TES was modeled for 1 year of space heating and cooling loads for a residential building in three different climates in the United States. The vapor compression system of the HP was modified to integrate with TES, and all heat transfer to and from the TES was mediated by the HP. A single PCM was used for heating and cooling, and the PCT and TES capacity were varied to observe their effects on the building’s energy consumption, peak load shifting and cost savings. The maximum reduction in electric consumption, utility cost and peak electric demand were achieved at a PCT of 30 °C for New York City and 20 °C for Houston and Birmingham. Peak energy consumption in Houston, New York City, and Birmingham was reduced by 47%, 53%, and 70%, respectively, by shifting peak load using a time-of-use utility schedule. TES with 170 MJ storage capacity allowed for maximum demand shift from on-peak to off-peak hours, with diminishing returns once the TES capacity equaled the daily building thermal loads experienced during the most extreme ambient conditions.
Chronic Stress-Related Osteosarcopenic Obesity: A Common Modern Syndrome Requiring Sustained Lifestyle Changes and Stress Management
Authors: Papadopoulou-Marketou Nektaria , Papageorgiou Anna , Chrousos George P.
Published: May 2023

Through several pathological mechanisms, chronic stress contributes to the development of “osteosarcopenic obesity”, a clinical syndrome that includes impairments in the structure and function of a patient’s bones, skeletal muscles, and adipose tissue. This syndrome, which could be alternatively called “chronic stress and inflammation syndrome”, has its genesis in early life and, by the age of 50–60 years, affects up to two-thirds of Western populations. Chronic psycho-socioeconomic stress and lifestyle factors, such as a sedentary life, poor quality nutrition, irregular daily schedules, and inadequate sleep, which all act on a genetic and epigenetic predisposition background, play essential pathogenic roles in the development of this widespread syndrome. Key pathogenic mediators are those of the stress system and inflammatory reaction. Lifestyle changes, in combination with stress management, can prevent, arrest, or reverse this debilitating syndrome.
Prediction of Regional Forest Biomass Using Machine Learning: A Case Study of Beijing, China
Authors: Liu Jincheng , Yue Chengyu , Pei Chenyang , Li Xuejian , Zhang Qingfeng
Published: May 2023

Dynamic changes in forest biomass are closely related to the carbon cycle, climate change, forest productivity and biodiversity. However, most previous studies mainly focused on the calculation of current forest biomass, and only a few studies attempted to predict future dynamic changes in forest biomass which obtained uncertain results. Therefore, this study comprehensively considered the effects of multi-stage continuous survey data of forest permanent sample plots, site condition factors and corresponding meteorological factors using Beijing as an example. The geographic detector method was used to screen the key interfering factors that affect the growth of forest biomass. Then, based on the back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) learning methods, 80% of the sample data were extracted to train the model, and thereby verify the prediction accuracy of different modeling methods using different training samples. The results showed that the forest biomass prediction models based on both the machine learning algorithms had good fitting accuracy, and there was no significant difference in the prediction results between the two models. However, the SVM model was better than the BP-ANN. While the BP-ANN model provided more volatile predictions, and the accuracy was above 80%, the prediction results of the SVM model were relatively stable, and the accuracy was above 90%. This study not only provides good technical support for the scientific estimation of regional forest biomass in the future, but also offers reliable basic data for sustainable forest management, planning decisions, forest carbon sequestration and sustainable development.
Integrated Learning-Based Pest and Disease Detection Method for Tea Leaves
Authors: Wang Yinkai , Xu Renjie , Bai Di , Lin Haifeng
Published: May 2023

Currently, the detection of tea pests and diseases remains a challenging task due to the complex background and the diverse spot patterns of tea leaves. Traditional methods of tea pest detection mainly rely on the experience of tea farmers and experts in specific fields, which is complex and inefficient and can easily lead to misclassification and omission of diseases. Currently, a single detection model is often used for tea pest and disease identification; however, its learning and perception capabilities are insufficient to complete target detection of pests and diseases in complex tea garden environments. To address the problem that existing target detection algorithms are difficult to identify in the complex environment of tea plantations, an integrated learning-based pest detection method is proposed to detect one disease (Leaf blight) and one pest (Apolygus lucorμm), and to perform adaptive learning and extraction of tea pests and diseases. In this paper, the YOLOv5 weakly supervised model is selected, and it is found through experiments that the GAM attention mechanism’s introduction on the basis of YOLOv5’s network can better identify the Apolygus lucorμm; the introduction of CBAM attention mechanism significantly enhances the effect of identifying Leaf blight. After integrating the two modified YOLOv5 models, the prediction results were processed using the weighted box fusion (WBF) algorithm. The integrated model made full use of the complementary advantages among the models, improved the feature extraction ability of the model and enhanced the detection capability of the model. The experimental findings demonstrate that the tea pest detection algorithm effectively enhances the detection ability of tea pests and diseases with an average accuracy of 79.3%. Compared with the individual models, the average accuracy improvement was 8.7% and 9.6%, respectively. The integrated algorithm, which may serve as a guide for tea disease diagnosis in field environments, has improved feature extraction capabilities, can extract more disease feature information, and better balances the model’s recognition accuracy and model complexity.
Comparative Study on Ginger Powder and Ginger Extract Nanoparticles: Effects on Growth, Immune–Antioxidant Status, Tissue Histoarchitecture, and Resistance to Aeromonas hydrophila and Pseudomonas putida Infection in Oreochromis niloticus
Authors: Ahmed Shaimaa A. A. , El-Murr Abdelhakeem , Abd Elhakim Yasser , Metwally Mohamed M. , Gharib Amany Abd El Aziz , Amer Shimaa A. , Younis Elsayed M. , Abdel-Warith Abdel-Wahab A. , Davies Simon J. , Khalil Enas N. M.
Published: May 2023

A 10 week feeding trial was conducted to evaluate the potential effects of ginger powder (GP) and ginger extract nanoparticles (GNPs) on the growth parameters, digestive enzymes (lipase and amylase) activities, blood hematology, blood biochemical indices, immune indices (interleukin 10, immunoglobulin M, nitric oxide, and lysozymes), antioxidant activity, histological characteristics of kidney, spleen, liver, and intestine, and resistance to Aeromonas hydrophila or Pseudomonas putida infection in Nile tilapia, Oreochromis niloticus. Fish (n = 225, 27.01 ± 0.15 g) were stocked in 15 glass tanks (50 × 40 × 60 cm) and randomly allocated to five experimental treatments (TRTs) in triplicate (15 fish/replicate, 45 fish/TRT), consisting of five isocaloric–isonitrogenous diets. The treatments comprised the basal diet (1) without any additives (control group, CON), (2) with 0.5% GP (GP0.5), (3) with 1% GP (GP1), (4) with 0.5% GNPs (GNPs0.5), and (5) with 1% GNPs (GNPs1). Fish were manually fed to satiety three times a day (at 9 a.m., 12 p.m., and 2 p.m.). Fish were weighed at the start of the experiment, then the body weight, weight gain, feed intake, and feed conversion ratio were determined at the end of the experiment. At the end of the feeding period, 15 fish/TRT were intraperitoneally inoculated with two pathogenic bacterial strains (A. hydrophila or P. putida) in two separate challenge tests. Blood samples were collected from each TRT at two aliquots for hematological and biochemical analysis at the end of the feeding period. A significant improvement in fish growth was observed in GP and GNPs TRTs compared to the control group. There were no significant changes in the total amount of feed intake/fish in response to the experimental diets. Diets enriched with GNPs, particularly the GNPs1 TRT, resulted in a significant increase (p < 0.05) in digestive enzyme activity (lipase and amylase), serum growth hormone level, proteinogram, and immune indices (lysozyme, immunoglobulin M, interleukin 10, and nitric oxide). In addition, a significant increase in hepatic antioxidant enzymes (superoxide dismutase, reduced glutathione, and catalase) was observed in fish fed with GNPs-enriched diets. Survival percentages following bacterial challenge were higher in GNPs1, followed by GP1 and GNPs0.5 TRTs. Normal histomorphology was found in liver, kidney, and spleen tissues in all experimental TRTs. We conclude that GP and GNPs could be included in Nile tilapia diets for promoting fish growth, immunity, antioxidant status, and disease resistance without harming organ functions. In particular, the most effective treatment was GNPs1.
A Sufficient and Necessary Condition for the Power-Exponential Function 1+1xαx to Be a Bernstein Function and Related nth Derivatives
Authors: Cao Jian , Guo Bai-Ni , Du Wei-Shih , Qi Feng
Published: May 2023

In the paper, the authors find a sufficient and necessary condition for the power-exponential function 1+1xαx to be a Bernstein function, derive closed-form formulas for the nth derivatives of the power-exponential functions 1+1xαx and (1+x)α/x, and present a closed-form formula of the partial Bell polynomials Bn,k(H0(x),H1(x),⋯,Hn−k(x)) for n≥k≥0, where Hk(x)=∫0∞eu−1−ueuuk−1e−xudu for k≥0 are completely monotonic on (0,∞).
Detailed Courses and Pathological Findings of Colonic Perforation without Diverticula in Sisters with Musculocontractural Ehlers–Danlos Syndrome Caused by Pathogenic Variant in CHST14 (mcEDS-CHST14)
Authors: Kobayashi Tomoko , Fujishima Fumiyoshi , Tokodai Kazuaki , Sato Chiaki , Kamei Takashi , Miyake Noriko , Matsumoto Naomichi , Kosho Tomoki
Published: May 2023

Musculocontractural Ehlers–Danlos syndrome (mcEDS) is a heritable connective tissue disorder characterized by multiple congenital malformations and progressive connective-tissue-fragility-related manifestations in the cutaneous, skeletal, cardiovascular, visceral, ocular, and gastrointestinal systems. It is caused by pathogenic variants in the carbohydrate sulfotransferase 14 gene (mcEDS-CHST14) or in the dermatan sulfate epimerase gene (mcEDS-DSE). As gastrointestinal complications of mcEDS-CHST14, diverticula in the colon, small intestine, or stomach have been reported, which may lead to gastrointestinal perforation, here, we describe sisters with mcEDS-CHST14, who developed colonic perforation with no evidence of diverticula and were successfully treated through surgery (a resection of perforation site and colostomy) and careful postoperative care. A pathological investigation did not show specific abnormalities of the colon at the perforation site. Patients with mcEDS-CHST14 aged from the teens to the 30s should undergo not only abdominal X-ray photography but also abdominal computed tomography when they experience abdominal pain.