bluuebunny/crossref_metadata_2025 huggingface dataset:
Deposition Strategies for Bar Intersections Using Dot-by-Dot Wire and Arc Additive Manufacturing
Authors: Grossi Niccolò , Lazzeri Flavio , Venturini Giuseppe
Published: February 2025

Dot-by-dot Wire and Arc Additive Manufacturing (WAAM) is a promising technique for producing large-scale lattice structures, offering significant benefits in terms of deposition rate and material utilization. This study explores strategies for fabricating bar intersections using the dot-by-dot WAAM technology, focusing on creating robust and predictable structures without requiring parameter modifications or real-time monitoring during the deposition. Two different deposition strategies were proposed, that can be, at least geometrically, applied to a general intersection with multiple bars with different angles. In this work such strategies were only experimentally tested on two-bar intersections, assessing their performance in terms of geometrical accuracy, symmetry, and material efficiency. Strategies which utilize layer-by-layer deposition with multiple overlapping dots, called B here, demonstrated the best results in terms of the geometrical features in the intersection zone, assessed by different metrics obtained through an analysis of pictures, such as low asymmetry and high material volume in the intersection zone. In addition, the findings suggest that removing cooling pauses during the deposition of multiple dots on the same layer slightly improves the joint by minimizing excess material buildup. The proposed approach offers a scalable framework for optimizing intersection deposition, paving the way for improved large-scale metal lattice structure manufacturing.
The Heterogeneity of Ornamental Plants in Nurseries Increases the Chance of Finding New Hosts for Phytophthora
Authors: Soto-Plancarte Alejandro , Díaz-Celaya Marlene , Rodríguez-Alvarado Gerardo , Fernández-Pavía Yolanda Leticia , Silva-Rojas Hilda Victoria , Pedraza-Santos Martha Elena , Salgado-Garciglia Rafael , Bourret Tyler Baldwin , Fernández-Pavía Sylvia Patricia
Published: February 2025

The production of ornamental plants in Mexico represents a job-generating activity that has grown in recent years; however, it is adversely affected by phytosanitary issues, notably those induced by Phytophthora. Studies of Phytophthora in ornamental nurseries are scarce in Mexico. The aim in this study was to identify Phytophthora species from selected ornamental plant nurseries in Mexico as potential new hosts. Samples of 13 genera diseased plant tissue and soil were collected from eight nurseries in Mexico during 2009–2010. Based on morphology and sequences of ITS rDNA, the 19 isolates obtained were identified as P. cactorum, P. capsici, P. cinnamomi, P. drechsleri, P. kelmanii, P. nicotianae, and P. tropicalis. Additional loci were sequenced to support species determinations within the P. capsici species complex; some of these isolates could not be confirmed as belonging to any described species, and one appeared to be an interspecific hybrid. This is the first report of P. kelmanii in Mexico; this is noteworthy due to being a broad host range, similar to most of the other species encountered. Evidence of nursery-grown plants serving as a Phytophthora vector to a home garden has been reported for the first time in Mexico. Cestrum nocturnum and Solanum ovigerum are new hosts for Phytophthora worldwide.
Evaluation of Clastogenic and Aneugenic Action of Two Bio-Insecticides Using Allium Bioassay
Authors: Duta-Cornescu Georgiana , Dugala Maria Liliana , Constantin Nicoleta , Pojoga Maria-Daniela , Simon-Gruita Alexandra
Published: February 2025

It is well known that modern agriculture would not be able to meet the current demand for food without the help of pesticides. However, conventional pesticides have been proven to be extremely harmful to the environment, to the species they are applied to, and, ultimately, to humans. As a result, bio-pesticides have been introduced in recent years and include natural substances that control pests, such as biochemical pesticides, microorganisms used as pest control agents (microbial pesticides), and pesticide substances produced by plants containing added genetic material, known as plant-incorporated protectants (PIPs). Although these are natural products, their widespread use has led to an increased presence in the environment, raising concerns regarding their potential impact on both the environment and human health. The aim of our study was to determine the phyto- and cytogenotoxicity caused by two insecticides, both certified for use in ecological agriculture: one biochemical (BCP) and the other microbial (MP), which were applied in three concentrations (the maximum recommended concentration by the manufacturers (MRFC), 1.5X MRFC, and 2X MRFC) to the meristematic root tissues of Allium cepa. The results were compared to a negative control (tap water) and a positive control (a chemical pesticide (CP) containing mainly Deltamethrin). Phytotoxic and cytogenotoxic effects were analyzed at two time intervals (24 and 48 h) by measuring root length, growth percentage, root growth inhibition percentage (phytotoxicity tests), and micronuclei frequency and chromosome aberrations (anaphase bridges, chromosomal fragments, anaphase delays, sticky chromosomes, laggard/vagrant chromosomes) (cytogenotoxicity analyses), respectively. The tests conducted in this study showed that the microbial insecticide provides greater safety when applied, even at higher doses than those recommended by the manufacturers, compared with the biochemical insecticide, whose effects are similar to those induced by the chemical pesticide containing Deltamethrin. However, the results suggest that both insecticides have clastogenic and aneugenic effects, highlighting the need for prior testing of any type of pesticide before large-scale use, especially since the results of the A. cepa tests showed high sensitivity and good correlation when compared to other test systems, e.g., mammals.
Improving Simulation Model Accuracy for Friction Stir Welding of AA 2219
Authors: Brooks Kennen , Ramos Bryan , Prymak David J. , Nelson Tracy W. , Miles Michael P.
Published: February 2025

Modeling of friction stir welding (FSW) is challenging, as there are large gradients in both strain rate and temperature (typically between 450 and 500 °C in aluminum alloys) that must be accounted for in the constitutive law of the material being joined. Constitutive laws are most often calibrated using flow stresses from hot compression or hot torsion testing, where strain rates are much lower than those seen in the stir zone of the FSW process. As such, the current work employed a recently developed method to measure flow stresses at high strain rates and temperatures in AA 2219-T67, and these data were used in the development of a finite element (FE) simulation of FSW. Because heat generation during FSW is primarily a function of friction between the rapidly spinning tool and the plate, the choice of friction law and associated parameters were also studied with respect to FE model predictions. It was found that the Norton viscoplastic friction law provided the most accurate modeling results, for both the transient and steady-state phases of an FSW plunge experiment. It is likely that the superior performance of the Norton law was its ability to account for temperature and rate sensitivity of the plate material sheared by the tool, while the Tresca-limited Coulomb law favored contact pressure, with essentially no temperature or rate dependence of the local material properties. With optimized friction parameters and more accurate flow stresses for the weld zone, as measured by a high-pressure shear test, a 65% overall reduction in model error was achieved, compared to a model that employed a material law calibrated with hot compression or hot torsion test results. Model error was calculated as an equally weighted comparison of temperatures, torques, and forces with experimentally measured values.
Metaheuristics Algorithm-Based Optimization for High Conductivity and Hardness CuNi2Si1 Alloy
Authors: Konieczny Jarosław , Labisz Krzysztof , Ürgün Satılmış , Yiğit Halil , Fidan Sinan , Bora Mustafa Özgür , Atapek Ş. Hakan
Published: February 2025

The optimization of CuNi2Si1 alloy’s mechanical and electrical properties was achieved through a combination of experimental approaches and metaheuristic algorithms. Optimizing hardness and electrical conductivity through a variation in aging temperature (450–600 °C) and aging duration (1–420 min) was taken under consideration in the present work. Cold rolling with 50% strain after solution annealing aided in microstructure refinement and accelerated Ni2Si precipitates’ development, and property improvement increased. Optimum temperature and holding period were 450 °C and 30 min, respectively, with 266 HV and 13 MS/m and 167 HV and 11.2 MS/m for non-deformed samples, respectively. SPBO, genetic algorithm (GA), and particle swarm optimization (PSO) metaheuristic algorithms were considered, and SPBO exhibited the best prediction accuracy. SPBO predicted 450 °C for 61.75 min, and experimental testing exhibited 267 HV and 14 MS/m, respectively. Polynomial regressions with 0.98 and 0.96 values for R2 confirmed these values’ accuracy. According to this work, computational optimization proves effective in optimizing development and property tailoring for application in industries including aerospace and electrical engineering.
Default Priors in a Zero-Inflated Poisson Distribution: Intrinsic Versus Integral Priors
Authors: Hong Junhyeok , Kim Kipum , Kim Seong W.
Published: February 2025

Prior elicitation is an important issue in both subjective and objective Bayesian frameworks, where prior distributions impose certain information on parameters before data are observed. Caution is warranted when utilizing noninformative priors for hypothesis testing or model selection. Since noninformative priors are often improper, the Bayes factor, i.e., the ratio of two marginal distributions, is not properly determined due to unspecified constants contained in the Bayes factor. An adjusted Bayes factor using a data-splitting idea, which is called the intrinsic Bayes factor, can often be used as a default measure to circumvent this indeterminacy. On the other hand, if reasonable (possibly proper) called intrinsic priors are available, the intrinsic Bayes factor can be approximated by calculating the ordinary Bayes factor with intrinsic priors. Additionally, the concept of the integral prior, inspired by the generalized expected posterior prior, often serves to mitigate the uncertainty in traditional Bayes factors. Consequently, the Bayes factor derived from this approach can effectively approximate the conventional Bayes factor. In this article, we present default Bayesian procedures when testing the zero inflation parameter in a zero-inflated Poisson distribution. Approximation methods are used to derive intrinsic and integral priors for testing the zero inflation parameter. A Monte Carlo simulation study is carried out to demonstrate theoretical outcomes, and two real datasets are analyzed to support the results found in this paper.
Fatigue Detection Algorithm for Nuclear Power Plant Operators Based on Random Forest and Back Propagation Neural Networks
Authors: Jiang Yuhang , Li Junsong , Zhang Yu
Published: February 2025

This article proposes a fatigue detection algorithm for nuclear power plant control room operators based on random forest and BP neural networks, specifically targeting the control room scenario. This algorithm is capable of detecting fatigue-related operations in a timely manner, which is crucial for ensuring the safe operation of nuclear power plants. First, the random forest algorithm is used to classify the feature data according to different scenarios. Second, the data are distributed to different back propagation neural networks for prediction based on the scenario. Finally, experimental validation is conducted using a reactor simulation system. The results show that the algorithm achieves a recognition accuracy of 0.82, an accuracy of 0.69, a recall rate of 0.64, and an F1-Score of 0.66, indicating that the proposed algorithm has practical value for detecting operator fatigue in nuclear power plants. Compared to physiological data-based detection methods, it is simple, convenient, cost-effective, and does not interfere with operators.
Random Variables Aren’t Random
Authors: Vos Paul W.
Published: February 2025

This paper examines the foundational concept of random variables in probability theory and statistical inference, demonstrating that their mathematical definition requires no reference to randomization or hypothetical repeated sampling. We show how measure-theoretic probability provides a framework for modeling populations through distributions, leading to three key contributions. First, we establish that random variables, properly understood as measurable functions, can be fully characterized without appealing to infinite hypothetical samples. Second, we demonstrate how this perspective enables statistical inference through logical rather than probabilistic reasoning, extending the reductio ad absurdum argument from deductive to inductive inference. Third, we show how this framework naturally leads to an information-based assessment of statistical procedures, replacing traditional inference metrics that emphasize bias and variance with information-based approaches that describe the families of distributions used in parametric inference better. This reformulation addresses long-standing debates in statistical inference while providing a more coherent theoretical foundation. Our approach offers an alternative to traditional frequentist inference that maintains mathematical rigor while avoiding the philosophical complications inherent in repeated sampling interpretations.
Use Cases of Machine Learning in Queueing Theory Based on a GI/G/K System
Authors: Efrosinin Dmitry , Vishnevsky Vladimir , Stepanova Natalia , Sztrik Janos
Published: February 2025

Machine learning (ML) in queueing theory combines the predictive and optimization capabilities of ML with the analytical frameworks of queueing models to improve performance in systems such as telecommunications, manufacturing, and service industries. In this paper we give an overview of how ML is applied in queueing theory, highlighting its use cases, benefits, and challenges. We consider a classical GI/G/K-type queueing system, which is at the same time rather complex for obtaining analytical results, consisting of K homogeneous servers with an arbitrary distribution of time between incoming customers and equally distributed service times, also with an arbitrary distribution. Different simulation techniques are used to obtain the training and test samples needed to apply the supervised ML algorithms to problems of regression and classification, and some results of the approximation analysis of such a system will be needed to verify the results. ML algorithms are used also to solve both parametric and dynamic optimization problems. The latter is achieved by means of a reinforcement learning approach. It is shown that the application of ML in queueing theory is a promising technique to handle the complexity and stochastic nature of such systems.
An Adaptive GPR-Based Multidisciplinary Design Optimization of Structural and Control Parameters of Intelligent Bus for Rollover Stability
Authors: Wang Tingting , Shao Xu , Qin Dongchen , Huang Kun , Yao Mingkuan , Duan Yuechen
Published: February 2025

Considering the influence of high-speed obstacle avoidance trajectory in the optimization design stage of intelligent bus aerodynamic shape. A collaborative optimization method aiming at aerodynamic structure and trajectory control system for intelligent bus rollover stability is proposed to reduce the interference of lateral aerodynamic load caused by large bus side area on driving stability and improve the rollover safety of intelligent bus in high-speed obstacle avoidance process. At the conceptual design stage, a multidisciplinary co-design optimization frame of aerodynamics/dynamics/control is built, and an adaptive Gaussian Process Regression approximate modeling method is proposed to establish an approximate model of high-precision and high-efficiency rollover evaluation index with rollover stability as the optimization objective and obstacle avoidance safety and resistance to crosswind interference as constraints. Taking rollover stability and obstacle avoidance safety as the optimization objectives, the integrated design of static structural parameters and dynamic control parameters of intelligent buses is carried out. The results show that the proposed MDO method can obtain the aerodynamic shape of the vehicle body with low crosswind sensitivity and a safe and stable obstacle avoidance trajectory. Compared with the initial trajectory, the peak lateral load transfer rate during the obstacle avoidance process decreases by 33.91%, which significantly reduces the risk of rollover. Compared with the traditional serial optimization method, the proposed co-design optimization method has obvious advantages and can further improve the driving safety performance of intelligent buses.
Integral-Based Memory Event-Triggered Controller Design for Uncertain Neural Networks with Control Input Missing
Authors: Wang Ping , Wang Zhen , Xu Haiyang
Published: February 2025

In this paper, the controller design problem for uncertain neural networks (NNs) with control input missing is addressed under an event-triggered (ET) scheme. First, under the zero-input method, the closed-loop system is modeled as a switched system which includes a stable subsystem and an unstable subsystem. Next, a novel integral-based memory event-triggered (IMET) scheme is designed, which can prevent Zeno behavior. The proposed IMET scheme is designed over a specified memory interval; thus, it can make full use of the historical state information, thereby reducing the adverse impact caused by packet dropouts. Then, with the analysis method of switched systems, a piecewise time-dependent Lyapunov functional is designed, and low conservative conditions are derived to ensure the exponential stability of the switched closed-loop system. Meanwhile, the constraints on the packet loss rate and the average dwell time are established. Moreover, the design of the controller gain is also given. Finally, the feasibility of IMET is verified using an example.
Marine Algae Polysaccharides: An Overview of Characterization Techniques for Structural and Molecular Elucidation
Authors: Lesco Kaitlin C. , Williams S. Kim R. , Laurens Lieve M. L.
Published: February 2025

Polysaccharides make up a large portion of the organic material from and in marine organisms. However, their structural characterization is often overlooked due to their complexity. With many high-value applications and unique bioactivities resulting from the polysaccharides’ complex and heterogeneous structures, dedicated analytical efforts become important to achieve structural elucidation. Because algae represent the largest marine resource of polysaccharides, the majority of the discussion is focused on well-known algae-based hydrocolloid polymers. The native environment of marine polysaccharides presents challenges to many conventional analytical techniques necessitating novel methodologies. We aim to deliver a review of the current state of the art in polysaccharide characterization, focused on capabilities as well as limitations in the context of marine environments. This review covers the extraction and isolation of marine polysaccharides, in addition to characterizations from monosaccharides to secondary and tertiary structures, highlighting a suite of analytical techniques.
Paraneoplastic Syndromes in Gallbladder Cancer: A Systematic Review
Authors: Lau Beth Shin Rei , Chua Nevin Yi Meng , Ong Wee Teck , Singh Harjeet , Luvira Vor , Takaori Kyoichi , Shelat Vishal G.
Published: February 2025

Background and Objectives: Gallbladder cancer (GBC) is a biologically aggressive malignancy characterised by poor survival outcomes often attributed to delayed diagnosis due to nonspecific clinical presentations. Paraneoplastic syndromes (PNSs), atypical symptoms caused by cancer itself, may serve as valuable indicators for timely diagnosis, particularly in malignancies with nonspecific features. Understanding the manifestations of PNSs in GBC is, therefore, critical. This systematic review collates case studies documenting the association of PNS with GBC, including subsequent management and clinical outcomes. Materials and Methods: A comprehensive search of PubMed, Embase, CINAHL, Web of Science, and Cochrane Library databases yielded 49 relevant articles. Upon searching other information sources, two more relevant articles were identified via citation sources. Results: The paraneoplastic syndromes were classified according to haematological (leukocytosis), dermatological (inflammatory myositis like dermatomyositis and polymyositis, acanthosis nigricans, Sweet’s syndrome, exfoliative dermatitis), neurological, metabolic (hypercalcemia, hyponatremia), and others (chorea). The analysis included the age, sex, and country of origin of the patient, as well as the time of PNS diagnosis relative to GBC diagnosis. Furthermore, common presenting complaints, investigations, and effectiveness of treatment modalities using survival time were assessed. Conclusions: While PNS management can offer some benefits, oncologic outcomes of GBC are largely poor. The majority of PNS in GBC are reported in advanced stages, and, hence, PNS has a minimal role in early diagnosis. PNS management can improve a patient’s quality of life, and thus recognition and treatment are important considerations in the holistic management of GBC patients.
Progress in Polycrystalline SiC Growth by Low Pressure Chemical Vapor Deposition and Material Characterization
Authors: Gavalas Michail , Gallou Yann , Chaussende Didier , Blanquet Elisabeth , Mercier Frédéric , Zekentes Konstantinos
Published: February 2025

The purpose of this paper is to give a review on the state of the art of polycrystalline SiC material grown by low-pressure chemical vapor deposition (LPCVD). Nowadays, LPCVD is the main technique used for the deposition of polycrystalline SiC, both in academic research and industry. Indeed, the LPCVD technique is today the most mature technique to grow high purity polycrystalline thin films with controlled thickness and structure over a large area (>50 cm) and/or 3D substrate. Its ability to have a high degree of modification on the growth conditions and the chosen precursor system allows the deposition of polycrystalline SiC films in various substrates with tailored properties according to the desired application. After a short introduction on the SiC material and its growth by the LPCVD technique, a review of theoretical studies (thermodynamics and kinetics) related to the CVD SiC growth process is given. A synthesis of the experimental studies is made focusing on the effect of the growth conditions on the properties of the deposited SiC polycrystalline material. Despite the numerous results, a full understanding of them is limited due to the complexity of the LPCVD process and the polycrystalline SiC structure. The conclusions show that the growth conditions, like temperature, chamber pressure, (C/Si)(g), (Cl/Si)(g), and doping have an impact on the microstructure and on the corresponding properties of the polycrystalline SiC films. Future perspectives are given in order to improve our understanding on the polycrystalline–SiC–LPCVD process and to enable the growth of tailor-made polycrystalline SiC films for future applications.
Simultaneous Quantification of Bioactive Triterpene Saponins Calenduloside E and Chikusetsusaponin IVa in Different Plant Parts of Ten Amaranthaceae Species by UPLC-ESI-MS/MS Method
Authors: Grabowska Karolina , Żmudzki Paweł , Galanty Agnieszka , Podolak Irma
Published: February 2025

Calenduloside E (CE) and chikusetsusaponin IVa (ChIVa) are triterpene saponins with multidirectional bioactivity. In this study, the contents of CE and ChIVa were determined in the roots, stems, leaves, and fruits of ten wild-growing species of Amaranthaceae. To achieve optimal extraction conditions for both saponins, maceration, shaking-assisted maceration, and ultrasound-assisted and heat reflux extraction were compared. A sensitive, specific, and rapid UPLC-MS/MS method was developed and validated for the simultaneous quantification of CE and ChIVa. The results showed that CE and ChIVa coexisted in most of the species analyzed, except for Ch. hybridum. For the first time, the presence of CE and ChIVa was noted in L. polysperma, A. patula, B. bonus-henricus, O. rubra, and O. glauca. Of the species analyzed, the highest ChIVa content was found in the fruit of A. sagittata (13.15 mg/g dw), L. polysperma (12.20 mg/g dw), and Ch. album (10.0 mg/g dw), and in the fruit and roots of Ch. strictum (5.52 and 7.77 mg/g dw, respectively). The highest amount of CE was determined in the fruit of A. sagittata (7.84 mg/g dw) and Ch. strictum (6.54 mg/g dw). These saponin-abundant plant parts of Amaranthaceae spp. may be considered convenient sources of these bioactive saponins.
Application of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion
Authors: Rutland Harvey , You Jiseon , Liu Haixia , Bowman Kyle
Published: February 2025

This study explores the application of various machine learning (ML) models for the real-time prediction of the FOS/TAC ratio in microbial electrolysis cell anaerobic digestion (MEC-AD) systems using data collected during a 160-day trial treating brewery wastewater. This study investigated models including decision trees, XGBoost, support vector regression, a variant of support vector machine (SVM), and artificial neural networks (ANNs) for their effectiveness in the soft sensing of system stability. The ANNs demonstrated superior performance, achieving an explained variance of 0.77, and were further evaluated through an out-of-fold ensemble approach to assess the selected model’s performance across the complete dataset. This work underscores the critical role of ML in enhancing the operational efficiency and stability of bio-electrochemical systems (BES), contributing significantly to cost-effective environmental management. The findings suggest that ML not only aids in maintaining the health of microbial communities, which is essential for biogas production, but also helps to reduce the risks associated with system instability.
Adjunctive Effects of Diode Laser in Surgical Periodontal Therapy: A Narrative Review of the Literature
Authors: D’Albis Giuseppe , Forte Marta , Fioriello Maria Chiara , Artin Alexandra , Montaruli Anna , Di Grigoli Adriano , Kazakova Rada , Dimitrova Mariya , Capodiferro Saverio
Published: February 2025

The surgical procedures in the treatment of periodontitis generally aim to reduce pocket depth, improve teeth maintenance, and enhance long-term stability. Several approaches have been proposed over the years including different techniques, drugs, and medical devices, with the main purpose to minimalize the surgical procedure and improve both the outcomes and patient compliance. Among all the medical devices proposed in surgical and non-surgical periodontology, different laser wavelengths (e.g., Erbium: YAG, Erbium: CR, KTP, Diode) have been studied worldwide but generally with inconsistent results. Nowadays, the diode laser is one of the most used lasers among general dentists, also promoted as an adjunctive therapy in periodontology, mainly in non-surgical procedures. This study aims to review data emerging from the literature on the use of diode lasers and adjunctive therapy to the conventional periodontal surgery. This research was conducted using PubMed and Scopus search engines with a combination of relevant keywords, including “Surgical Periodontal Therapy”, “Periodontal Surgery”, “Periodontal Regenerative Surgery”, and “Surgical Treatment for Periodontal Disease” in combination with “Diode Laser”, “Diode-Driven Laser”, and “Diode Emission Laser”. Selected articles were carefully reviewed to identify studies reporting data on the effectiveness of diode lasers in periodontal surgery. Results of the current review suggest potential clinical benefits of diode laser-assisted periodontal surgery, as studies reported improvements in key parameters such as clinical attachment level (CAL), bleeding on probing (BOP), and plaque index (PI) postoperatively. Additionally, VAS scores, indicative of post-surgical discomfort, were lower for laser-treated sites, and a short-term reduction in periodontal pathogens was observed. However, the clinical outcomes significantly differ across the studies, and the evidence does not uniformly support a superior effectiveness of diode lasers as an adjunctive tool in surgical periodontology. While the data indicate that diode laser use may contribute to enhanced periodontal health and possibly accelerate healing in some cases, these findings should be interpreted cautiously, as further research, possibly multicentric and in large numbers but mandatory with well-defined protocols (stage of periodontitis, preliminary non-surgical procedures and results, laser wavelength and protocol of use, post-operative maintenance, follow-up clinical and radiological criteria) are surely needed to possibly validate the observations emerging from the current review and eventually to standardize clinical protocols in the future. Such limitations have been well addressed in this paper and are clearly discussed and essentially related to the focus on the total uncertainty of the literature and general caution.
High-Throughput Sequencing Enables Rapid Analyses of Nematode Mitochondrial Genomes from an Environmental Sample
Authors: Jain Akshita , Li Tongda , Wainer John , Edwards Jacqueline , Rodoni Brendan C. , Sawbridge Timothy I.
Published: February 2025

Mitochondrial genomes serve as essential tools in evolutionary biology, phylogenetics, and population genetics due to their maternal inheritance, lack of recombination, and conserved structure. Traditional morphological methods for identifying nematodes are often insufficient for distinguishing cryptic species complexes. This study highlights recent advancements in nematode mitochondrial genome research, particularly the impact of long-read sequencing technologies such as Oxford Nanopore. These technologies have facilitated the assembly of mitochondrial genomes from mixed soil samples, overcoming challenges associated with designing specific primers for long PCR amplification across different groups of parasitic nematodes. In this study, we successfully recovered and assembled eleven nematode mitochondrial genomes using long-read sequencing, including those of two plant-parasitic nematode species. Notably, we detected Heterodera cruciferae in Victoria, expanding its known geographic range within Australia. Additionally, short-read sequencing data from a previous draft genome study revealed the presence of the mitochondrial genome of Heterodera filipjevi. Comparative analyses of Heterodera mitogenomes revealed conserved protein-coding genes essential for oxidative phosphorylation, as well as gene rearrangements and variations in transfer RNA placement, which may reflect adaptations to parasitic lifestyles. The consistently high A+T content and strand asymmetry observed across species align with trends reported in related genera. This study demonstrates the utility of long-read sequencing for identifying coexisting nematode species in agricultural fields, providing a rapid, accurate, and comprehensive alternative to traditional diagnostic methods. By incorporating non-target endemic species into public databases, this approach enhances biodiversity records and informs biosecurity strategies. These findings reinforce the potential of mitochondrial genomics to strengthen Australia’s as well as the global biosecurity framework against plant-parasitic nematode threats.
The Effect of Deficit Irrigation on the Quality Characteristics and Physiological Disorders of Pomegranate Fruits
Authors: Porras-Jorge Rossana , Aguilar José Mariano , Baixauli Carlos , Bartual Julián , Pascual Bernardo , Pascual-Seva Nuria
Published: February 2025

This study assesses the impact of two regulated deficit irrigation (RDI) and one sustained deficit irrigation (SDI) strategies on the fruit quality characteristics of pomegranate (Punica granatum L.) compared to a fully irrigated control in a Mediterranean climate. Field trials were conducted over two growing seasons at the Cajamar Experimental Center in Paiporta, Valencia, Spain. The SDI strategy, which achieved considerable water savings of approximately 50%, led to a reduction in yield (both total and marketable), as well as a decrease in the size and unit weight of the fruits. However, it also produced arils with higher dry matter content and aril juice with higher soluble solids content, all without altering the maturity index. Notably, the SDI approach resulted in increased non-marketable production due to a higher incidence of cracking, particularly during the exceptionally hot and dry summer of 2023. Although the maturity index remained unchanged across the irrigation strategies, the SDI yielded a greater percentage of pink-red rind on marketable fruits compared to the other strategies. This is important because ‘Mollar de Elche’ pomegranates are typically harvested based on their external colour. Thus, the SDI strategy could allow for earlier harvesting, potentially enhancing the commercial value, as earlier harvests often command higher prices, which may partially offset some of the reduction in marketable yield. Conversely, both RDI strategies achieved a slight water saving without compromising marketable yield or the quality characteristics of the fruit.
We Are Not One, We Are Legion—Secular State in Mexico, Local Dynamics of a Federal Issue
Authors: Gaytan Alcala Felipe
Published: February 2025

The management of laicity in Mexico, legally and politically, is a federal issue that involves regulating the activities of Churches and religious communities in the public space, in their practices, rituals, and relations with the organs of the state. However, in recent years, the growing presence and activity of Churches at the local level has called into question the need to observe how laicity is managed by subnational governments, both state and municipal. Are there mechanisms at the local level to regulate the presence of religion in the public space? How are religious traditions presented as culturally managed? What are the demands of Churches on local authorities and what is their political relationship with them? How is the demand for religious freedom resolved locally without violating citizens’ other freedoms, such as the freedom of conscience in issues such as education, health, traffic, and freedom of expression? All this has put into perspective whether laicity and the secular state should continue to be a national dimension or whether it is necessary to rethink legal and political forms at the local level, building new frameworks of governance and governability. This text reviews the public management of laicity in eight entities of the country, which in turn is representative of the rest of the entities with their local variations. However, they generally move in the constant dimensions of religious diversity, interreligious councils, offices, or those in charge of religious affairs, and levels of municipal participation. The construction of a new laicity is then proposed, which does not exclude religion from the public agenda but rather a new secular perspective on the participation of religious communities in public affairs. From a Latin American perspective, Mexico is seen as an effective government regime that separates religion from politics, restricting the participation of religious organizations in the public agenda. However, at the local level, this regime is changing with the inclusion of faith-based organizations in politics. This will undoubtedly lead to a change in the historical concept, a reference point in the region. The term management of laicity refers to the regulation and administration of governments (services, legal support, spaces, and dialogues) with religious communities. Management (control, regulation, permits, sanctions, and recognition) is defined by law and in public policy towards religion from the federal government, but not in local governments that lack clear regulatory frameworks, intervention guidelines, and support, hence the emphasis on the term.
“For If There Is No Resurrection of the Dead, Then Christ Has Not Been Raised Either”: Wittgenstein and the Cognitive Status of Christian Belief Statements
Authors: Pichler Alois
Published: February 2025

The article engages in a critique of Wittgensteinian non-cognitivism about Christian belief statements but argues that Wittgenstein himself can only partially be classified under the non-cognitivist label. The article has three parts. First, it argues, contrary to a common view in the literature, that Wittgenstein moved not only from strong non-cognitivism to moderate non-cognitivism but also from general non-cognitivism towards partial cognitivism about Christian belief statements. Second, it examines three separate non-cognitivist Wittgensteinian claims about Christian belief statements and argues that these claims are untenable not only from a broad perspective, but also from the point of view of Wittgenstein’s Philosophical Investigations. The three claims are: (1) “The cognitive aspect of Christian belief statements is superstition only”, (2) “The cognitive aspect of Christian belief statements is parasitic on religious attitude only”, and (3) “The cognitive aspect of Christian belief statements is cognitive in surface grammar only”. Third, the article considers the case of believing Christian martyrs and follows the view that their religious belief might be cognitive only at the surface grammar level, through to its ultimate consequences. The article argues, using Wittgenstein’s Philosophical Investigations, that the non-cognitivist position ends up with a cognitive point about the correctness or incorrectness of the Christian martyr’s depth grammar.
Effective Practices for Implementing Quality Control Circles Aligned with ISO Quality Standards: Insights from Employees and Managers in the Food Industry
Authors: Lima Ana Beatriz Silva de , Becerra Claudia Editt Tornero , Feitosa Amanda Duarte , Albuquerque André Philippi Gonzaga de , Melo Fagner José Coutinho de , Medeiros Denise Dumke de
Published: February 2025

Quality control circles (QCCs) are a proven method for fostering continuous improvement through employee involvement. However, the implementation process and organizational impact of QCCs in the food industry remain underexplored. This case study evaluates the implementation of QCCs by examining the benefits and challenges perceived by employees and managers, assessing QCC alignment with ISO standards, and providing actionable recommendations to optimize QCC implementation. Using a mixed-methods approach, the employee findings indicate that QCCs promote continuous improvement, enhance productivity, foster a positive culture of quality, and strengthen engagement and responsibility for product and process quality. Employees felt that their ideas were valued and that they received constructive feedback from leadership. However, they also identified challenges related to training and resource availability. From a managerial perspective, the ISO diagnostic tool revealed a 78.28% compliance rate with the QCC program planning, quality procedures, action plans, quality management system alignment, and documentation. Non-conformities included insufficient monitoring solutions, absence of effectiveness indicators, lack of risk assessments, and insufficient resources. Although managers acknowledged benefits such as improved engagement and communication, challenges such as limited human resources, high demand, and resistance to change were also noted. This paper concludes with recommendations for enhancing future QCC cycles and for creating a structured implementation process.
Plasma Gasification of Medical Plastic Waste to Syngas in a Greenhouse Gas (CO2) Environment
Authors: Tamošiūnas Andrius , Milieška Mindaugas , Gimžauskaitė Dovilė , Aikas Mindaugas , Uscila Rolandas , Zakarauskas Kęstutis , Fendt Sebastian , Bastek Sebastian , Spliethoff Hartmut
Published: February 2025

The global coronavirus (COVID-19) pandemic in early 2020 caused the amount of medical waste, especially plastic waste, to increase. The pandemic exacerbated the plastic waste management problem, including the need to find more sustainable treatment methods. This study investigated the sustainable conversion of plastic waste (FFP2-type face masks) to syngas via pure CO2 plasma gasification to recover energy and reduce environmental pollution. A direct current (DC) thermal arc plasma torch of 40.6–68.4 kW power generated the plasma stream. Carbon dioxide (CO2), as a greenhouse gas (GHG), was used as the main plasma-forming gas and gasifying agent. The 140thermal feedstock input plasma gasification system was used in the study. The effect of the CO2-to-C ratio on the gasification performance efficiency was investigated. The best CO2 plasma gasification process performance was obtained at a CO2-to-C ratio of 2.34. In these conditions, the main syngas components (H2 + CO) comprised 80.46 vol.% (H2: 24.62 vol.% and CO: 55.84 vol.%) and the following values were seen for the heating value of the syngas (LHVsyngas: 13.88 MJ/Nm3), the syngas yield (3.13 Nm3/kgFFP2), the tar content in the syngas (23.0 g/Nm3), the carbon conversion efficiency (CCE: 70.6%), and the cold gas efficiency (CGE: 47.8%). Additionally, the plasma gasification process mass and energy balance were evaluated. It was demonstrated that CO2 plasma gasification could be a promising thermochemical treatment technology for sustainable plastic waste disposal and the simultaneous utilization of greenhouse gases, such as carbon dioxide.
Developing a Novel Robust Model to Improve the Accuracy of River Ecosystem Health Assessment in the Qinghai–Tibet Plateau
Authors: Xu Yuan , Li Yun , Wang Xiaogang , Zhang Jianmin , Zhang Zhengxian
Published: February 2025

River ecosystem health assessment (REHA) is crucial for sustainable river management and water security. However, existing REHA methodologies still fail to consider the multiple effects of input uncertainty, environmental stochasticity, and the decision-maker’s bounded rationality. Moreover, REHA studies primarily focused on plain areas, leaving the Qinghai–Tibet Plateau (QTP) understudied despite its ecosystems’ heightened fragility and complexity. To address these gaps, this study combined Pythagorean fuzzy sets with cloud modeling and proposed the Pythagorean fuzzy cloud (PFC) approach. Accordingly, a novel robust model (PFC-TODIM) was created by expanding the conventional TODIM method to the PFC algorithm. We provided an REHA indicator system tailored to the distinctive characteristics in the QTP, leveraging multisource data. River ecosystem health, driving mechanisms, and potential threats were investigated in the Lhasa River (LR) using the PFC-TODIM model. Results showed that the created model effectively took multiple uncertainties into consideration, thereby improving the REHA accuracy and robustness. In the LR, health conditions demonstrated substantial spatial disparities. Sampling sites of 28%, 48%, and 24% were subhealthy, healthy, and excellent, respectively. Findings showed that anthropogenic factors, such as dams, urban development, and fish release adversely affect river health and should be properly managed.
Consumer Perception of Sugar Kelp (Saccharina latissima) Addition to Soup
Authors: Gorman Mackenzie , Code Matthew , Stright Allison , Moss Rachael , McSweeney Matthew B.
Published: February 2025

Seaweed is a sustainable and nutritionally beneficial ingredient; however, consumers do not regularly eat it in North America. Sugar kelp is one variety of seaweed that is presently underutilized and this study will evaluate Atlantic Canadians’ sensory perception of sugar kelp addition to soup. Participants’ (n = 90) liking and sensory perception of seaweed addition to soup (control [no sugar kelp], 4% wt/wt, 6% wt/wt, 8% wt/wt and 10% wt/wt) was evaluated. A second sensory trial evaluated the amount of sugar kelp the participants (n = 83) would add to the soup if given the opportunity and their resulting sensory perception. The participants used hedonic scales, check-all-that-apply, and general labelled magnitude scales to evaluate the soup. The results identified how consumers perceive sugar kelp in soup, as well as their liking of sugar kelp in soup. In both trials, the participants indicated that sugar kelp could be added at approximately 6% wt/wt without impacting their acceptance. Liking of the soup’s flavour was negatively impacted by the sugar kelp addition; however, it did not impact the amount of soup participants consumed in the second trial. The sugar kelp addition increased the intensity of saltiness and umami at the 6% wt/wt addition level and lower, but at 8% wt/wt the soup was associated with pungency and off-flavours. The results suggest that sugar kelp addition to soup is acceptable at low levels.
Enhanced Data-Driven Machine Learning Models for Predicting Total Organic Carbon in Marine–Continental Transitional Shale Reservoirs
Authors: Peng Sizhong , Feng Congjun , Qiu Zhen , Zhang Qin , Liu Wen , Gao Wanli
Published: February 2025

Natural gas, as a sustainable and cleaner energy source, still holds a crucial position in the energy transition stage. In shale gas exploration, total organic carbon (TOC) content plays a crucial role, with log data proving beneficial in predicting total organic carbon content in shale reservoirs. However, in complex coal-bearing layers like the marine–continental transitional Shanxi Formation, traditional prediction methods exhibit significant errors. Therefore, this study proposes an advanced, cost- and time-saving deep learning approach to predict TOC in marine–continental transitional shale. Five well log records from the study area were used to evaluate five machine learning models: K-Nearest Neighbors (KNNs), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The predictive results were compared with conventional methods for accurate TOC predictions. Through K-fold cross-validation, the ML models showed superior accuracy over traditional models, with the DNN model displaying the lowest root mean square error (RMSE) and mean absolute error (MAE). To enhance prediction accuracy, δR was integrated as a new parameter into the ML models. Comparative analysis revealed that the improved DNN-R model reduced MAE and RMSE by 57.1% and 70.6%, respectively, on the training set, and by 59.5% and 72.5%, respectively, on the test set, compared to the original DNN model. The Williams plot and permutation importance confirmed the reliability and effectiveness of the enhanced DNN-R model. The results indicate the potential of machine learning technology as a valuable tool for predicting crucial parameters, especially in marine–continental transitional shale reservoirs lacking sufficient core samples and relying solely on basic well-logging data, signifying its importance for effective shale gas assessment and development.