bluuebunny/crossref_metadata_2025 huggingface dataset:
Isolation and identification of a novel Bacillus velezensis strain JIN4 and its potential for biocontrol of kiwifruit bacterial canker caused by Pseudomonas syringae pv. actinidiae
Authors: Zhao Xin , Zhai Yang , Wei Lin , Xia Fei , Yang Yuanru , Yi Yongjian , Wang Hongying , Qiu Caisheng , Wang Feng , Zeng Liangbin
Published: December 2024

Bacterial canker is a devastating disease in kiwifruit production, primarily caused by Pseudomonas syringae pv. actinidiae. In this study, a strain of Bacillus velezensis named JIN4, isolated from a kiwifruit branch, showed antagonistic activity. The B. velezensis JIN4 strain was identified based on its morphological, physiological and biochemical characteristics, 16S rDNA, and gyrA sequences. Furthermore, the complete genome of the strain was elucidated, revealing the presence of three genes that confer resistance to antibiotics, including tetracycline, lincomycin, and streptomycin. Additionally, a cluster of nine secondary metabolite synthesis genes was identified. In the laboratory, the JIN4 strain exhibited nitrogen (N) and phosphorus (P) production, demonstrating plant growth-promoting and broad-spectrum inhibitory activities against pathogenic fungi and bacteria. In the field, the JIN4 strain demonstrated effective colonization of kiwifruit, increased antioxidant enzyme activity, induced resistance in kiwifruit plants, and achieved a control efficiency of 60.22% against kiwifruit bacterial canker. These results indicate that B. velezensis JIN4 is a potential biocontrol agent against kiwifruit bacterial canker.
The additional value of self-reflection and feedback on therapy outcome: a pilot study
Authors: Kamp David , Blanker Cynthia , Hafkenscheid Anton , van Os Jim
Published: December 2024

Introduction

Over the past few decades, psychotherapy research was dominated by testing the efficacy of “brand name” therapeutic techniques and models. Another line of research however, suggests that common factors, such as the therapeutic alliance and empathy, might play a greater role in effective therapy than specific therapeutic techniques and models. Routine process monitoring (RPM), focusing on common factors, has emerged as a promising approach to enhance therapy outcomes. This ongoing feedback loop aims to improve the therapeutic alliance and to address countertransference issues, potentially leading to better therapy outcomes.

Methods

A total of 131 patients above the age of 18 were included into the sample. The design of the study consisted of three stages, in which two kinds of instruments to measure the therapeutic alliance and countertransference were administered, namely the Session Rating Scale (SRS) and a brief version of Impact Message Inventory-Circumplex (IMI-CS). The Outcome Rating Scale (ORS) was used as an outcome measure. Depending on the stage of the study, these three instruments were administered every time a patient had a psychotherapy session.

Results

Patients reported improved outcomes on the ORS in T2 compared to T1 (p = 0.011). Furthermore, a significant increase in the strength of the therapeutic relationship, as measured by the SRS, occurred between T2 and T3 (p = 0.031). A positive correlation was found between SRS scores and the “friendly” position on the IMI-CS (p = <0.000), while a negative correlation was found between SRS ratings and the “submissive” position on the IMI-CS (p = 0.019).

Discussion

Patients experienced improvements and might attribute part of their improvement to the relationship with their therapists, evaluating the therapeutic alliance more positively in the later stages of their treatment. Despite methodological challenges, such as varying treatment durations and pre-existing therapist-patient relationships, the findings highlight the value of patient engagement and therapist self-reflection. This study supports the utility of deliberate practice in psychotherapy, suggesting it can enhance therapeutic outcome and laying the groundwork for future research.
Inner engineering is associated with perceived improvements in relationship quality, interpersonal connections, and interpersonal compassion
Authors: Jafari Nashaw , Manvelian Atina , Mohamed Enas , Rayapuraju Akila , Orui Hibiki , Alkuhaimi Sultan , Reed Preeti Upadhyay , Subramaniam Balachundhar
Published: December 2024

Objectives

Interpersonal relationships are a cornerstone of wellbeing. Mindfulness-based practices have been shown to improve relationship quality and reduce perceived stress. Inner Engineering (IE) is a transformative program that includes meditative and yogic practices associated with improvements in mindfulness and wellbeing.

Methods

This cross-sectional observational study enrolled participants who were already registered for the Inner Engineering program to learn Shambhavi Mahamudra Kriya, a multi-component, 21-min meditation. Of the 356 participants who consented, 290 participants downloaded the mobile 3Cs app to participate. The enrolled participants were asked to complete self-reported electronic questionnaires at three timepoints: before the program, post-program, and 6 weeks after the program. The following measures were used: the Positive-Negative Relationship Quality (PN-RQ) scale, Interpersonal Mindfulness Scale (IMS), Perceived Stress Scale (PSS), Compassion Scale (CS), and Flourishing Measure. Linear mixed-effects models were used to analyze the survey data, with p-values of < 0.05 considered statistically significant.

Results

From the baseline to the 6-week follow-up after the program, the participants reported experiencing more positive qualities (p < 0.001, ηp2 = 0.12) and fewer negative qualities (p = 0.001, ηp2 = 0.08) in their relationships, increased interpersonal mindfulness (p < 0.001, ηp2 = 0.30), decreased stress levels (p < 0.001, ηp2 = 0.27), enhanced compassion for others (p < 0.001, ηp2 = 0.17), and overall personal wellbeing (p < 0.001, ηp2 = 0.25).

Conclusion

Participation in the Inner Engineering yoga program appears to be associated with improvements in relationships, interpersonal mindfulness, compassion, stress, and overall wellbeing.

Clinical trial registration

ClinicalTrials.gov, identifier: NCT05528978.
Effect of SNS addiction on prosocial behavior: mediation effect of fear of missing out
Authors: Xu Manjing , Liu Donglin , Yan Jinzhe
Published: December 2024

Purpose

This study examines the relationship between social networking sites addiction and pro-social behavior, considering the increasing importance of social networking sites in daily life. It explores the mediating role of Fear of Missing Out in this relationship and investigates the moderating role of basic psychological need satisfaction.

Research design, data, and methodology

This study employed a snowball sampling method to conduct an online survey among social network users in China. The proposed model was tested using regression analysis to interpret the results.

Results

Findings indicate a negative predictive effect of social networking sites addiction on prosocial behavior. Misplaced fear partially mediates this relationship. Basic psychological need satisfaction significantly moderates the mediating effect of Fear of Missing Out on the relationship between SNS addiction and prosocial behavior.

Practical implications

This study provides strategies for effectively preventing social networking sites addiction in real-world settings and mitigating its negative impact on individuals’ physical and mental health. It suggests interventions at four levels—individual, school, society, and government—to enhance basic psychological need satisfaction, thereby improving prosocial behavior and facilitating the promotion of interpersonal interactions and the equitable, harmonious development of society.
Exploring the relationship between trauma, mental health, and occupational performance in health science center students
Authors: Ahmad Feddah M. , Sajit Dina M.
Published: December 2024

Introduction

Attending university marks a pivotal yet stressful phase in students’ lives, characterized by significant adjustments to a new environment that can impact mental, emotional, and physical well-being. The journey through the acceptance and admissions process into university introduces substantial challenges, academic performance and changes to daily life. Such challenges and corresponding conditions can be intensified for students entering university with prior traumatic experiences.

Objective

The purpose the study is: (a) to clarify the factors affecting student mental health by cataloging the prevalence and types of traumatic events (including PTSD and complex PTSD) that students experience, and (b) examine the relationship between students’ mental health and occupational performance through the assessment of satisfaction with daily activities and quality of life (QoL).

Method

The research study utilized a descriptive cross-sectional design. Participants included students pursuing degrees in Medicine, Dentistry, Pharmacy, and Allied Health, (N = 223). Four self-administered questionnaires were employed: the International Trauma Questionnaire (ITQ), Beck’s Depression Inventory (BDI), the Self-Satisfaction of Daily Occupation (SDO), and World Health Organization Quality of Life Brief (WHOQoLBrief). Data was analyzed using descriptive statistics, and Spearman correlation.

Result

Results from ITQ revealed participants experienced highest indicators of trauma were 68% affective dysregulation, and 65%, in avoidance and self-organization. Significant negative correlations were found between BDI scores and the quality-of-life social domain and quality of life environment domain (p = 0.001). Regarding BDI scores highest at 23.3% (n = 52) of students reported mild mood disturbance. SDO indicated that satisfaction levels increased with medium activity levels but decreased with high activity levels. In addition, a negative correlation was also found between SDO and BDI scores. Furthermore, a positive correlation between satisfaction with daily occupation (activity level) and WHOQoLBrief both physical, p > 0.001 and psychological, p = 0.006 was identified.

Conclusion

This research investigates the cyclical impact of mental health on students’ daily activities, QoL, and occupational performance, particularly among those in Health Sciences Center. The study highlights how traumatic events and poor mental health contribute to dissatisfaction with daily tasks, which in turn leads to a decline in QoL and occupational performance emphasizing the importance of developing effective outreach strategies community.
The Canadian Reconciliation Barometer: a rigorous tool for tracking reconciliation in Canada
Authors: Starzyk K. B. , Neufeld K. H. S. , Efimoff I. H. , Fontaine A. S. M. , White E. E.-L. , Moran R. , Peachey D. , Fontaine L. S. , Welch M. A.
Published: December 2024

Indigenous peoples in Canada have resisted centuries of colonial harm. In response to their resurgence and calls for justice, Canada is now on what is likely to be a long and winding truth and reconciliation journey. To help monitor perceptions of reconciliation progress in a good way, our team of Indigenous and non-Indigenous researchers created the Canadian Reconciliation Barometer. In Study 1, we wrote 89 self-report items representing 13 factors of reconciliation, which reflected what we learned from Elders, Survivors, and reconciliation leaders. A national sample of 592 Indigenous and 1,018 non-Indigenous participants completed the initial item pool. Exploratory factor analyses indicated that a 13-factor model had excellent fit, with only two factors needing minor conceptual modifications. We retained 64 internally consistent items representing 13 factors of reconciliation: Good Understanding of the Past and Present, Acknowledgment of Government Harm, Acknowledgment of Residential School Harm, Acknowledgment of Ongoing Harm, Engagement, Mutually Respectful Relationships, Nation-to-Nation Relationships, Personal Equality, Systemic Equality, Representation and Leadership, Indigenous Thriving, Respect for the Natural World, and Apologies. In Study 2, a national sample of 599 Indigenous and 1,016 non-Indigenous participants completed the retained items. The hypothesized factors had excellent fit, and the factor structure did not differ between Indigenous and non-Indigenous participants. We conclude by discussing contributions to social-psychological conceptualizations of reconciliation and how to use the Canadian Reconciliation Barometer to monitor social change.
Improving collaboration between native Hawaiians and energy professionals to explore geothermal energy potential in Hawai‘i
Authors: Kamana Noa , Vaughan Mehana
Published: December 2024

Climate change is threatening vulnerable geographies including island nations, posing major economic and social risks. The development of renewables, specifically geothermal energy in the Hawaiian archipelago offers promising possibilities to achieve energy independence and security. However, geothermal development faces opposition from the Native Hawaiian community, stemming from Hawaiian cultural identity and beliefs that natural resources are akua (gods). Throughout Hawai‘i’s history Native Hawaiians have been marginalized in decision making processes, overlooking cultural implications of development. In this article we explore how to improve collaboration with Native Hawaiians to guide future geothermal energy development in Hawai‘i. We conducted seven semi-structured interviews with individuals from three groups: (1) Native Hawaiians community members, (2) Native Hawaiian geothermal professionals, and (3) non-native geothermal professionals. Using a grounded theory approach, we discovered the interviews emphasized the status of geothermal development, feelings towards geothermal, perceived obstacles, knowledge gaps and recommendations to improve development. Interviews were manually transcribed and analyzed to identify similarities and differences in responses. Due to the limited number of interviews, our results are not statistically robust, but do offer insights for equitable culturally grounded geothermal energy development. Findings include six key themes. (1) Renewable energy is critical for Hawai‘i’s future sustainability. (2) The primary obstacles facing geothermal energy expansion are not cultural. The largest obstacles are regulatory and financial. (3) Reasons and opportunities to support Geothermal exploration and expansion exist. Geothermal provides Hawai‘i a firm energy resource with a minimal ecological footprint. (4) Cultural implications surrounding geothermal are significant. (5) There is a need to rebuild collaboration and trust. Past developments failed to collaborate with local communities, destroying trust. (6) Opportunities to improve social / environmental justice and benefits for native/local communities forced to host green energy projects. Interview were then coded based on these overarching themes, extracting related text using Microsoft Excel. The information from this research allowed us to produce a narrative synthesis detailing areas of conflict and knowledge gaps and allowed us to create recommendations to improve collaboration between energy professionals and Native Hawaiians. This research has implications to guide potential green energy development in indigenous communities across the world.
Prevalence of bovine trypanosomosis and tsetse fly density in the Yem special district: a cross-sectional study
Authors: Bekele Dagim , Wang XiaoLong , Beshir Ahimedin , Terefe Endale
Published: December 2024

This study assesses the prevalence of bovine trypanosomes and the density of tsetse flies in the Yem Special District, Southern Ethiopia, highlighting the disease's significant impact on livestock health and agricultural productivity. Conducted between May 2022 and January 2023, the cross-sectional survey analyzed 960 blood samples for trypanosomes prevalence and tsetse fly density. Results revealed a 10.63% (9–12%) overall prevalence of bovine trypanosomes, with Trypanosoma congolense (5.83%) and Trypanosoma vivax (4.73%) as the predominant species, and 0.63% of mixed infection. Significant associations were found between trypanosomes and anemia, age, and sex, with higher prevalence rates observed in cattle with poor body condition scores and black-hair coats. The study identified three Glossina species, with Glossina morsitans submorsitans being the most prevalent. The findings underscore the need for integrated vector control strategies, community engagement in disease management, and further research using molecular techniques for deeper understanding and effective control measures. Collaboration among stakeholders is crucial for mitigating the disease's impact on livestock and human populations.
Linear Matrix Inequality-Based Design of Structured Sparse Feedback Controllers for Sensor and Actuator Networks
Authors: Kawano Yuta , Kobayashi Koichi , Yamashita Yuh
Published: December 2024

A sensor and actuator network (SAN) is a control system where many sensors and actuators are connected through a communication network. In a SAN with redundant sensors and actuators, it is important to consider choosing sensors and actuators used in control design. Depending on applications, it is also important to consider not only the choice of sensors/actuators but also that of communication channels in which some sensors/actuators are connected. In this paper, based on a linear matrix inequality (LMI) technique, we propose a design method for structured sparse feedback controllers. An LMI technique is one of the fundamental tools in systems and control theory. First, the sparse reconstruction problems for vectors and matrices are summarized. Next, two design problems are formulated, and an LMI-based solution method is proposed. Finally, two numerical examples are presented to show the effectiveness of the proposed method.
Iterative Application of UMAP-Based Algorithms for Fully Synthetic Healthcare Tabular Data Generation
Authors: Lázaro Carla , Angulo Cecilio
Published: December 2024

Building on a previously developed partially synthetic data generation algorithm utilizing data visualization techniques, this study extends the novel algorithm to generate fully synthetic tabular healthcare data. In this enhanced form, the algorithm serves as an alternative to conventional methods based on Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). By iteratively applying the original methodology, the adapted algorithm employs UMAP (Uniform Manifold Approximation and Projection), a dimensionality reduction technique, to validate generated samples through low-dimensional clustering. This approach has been successfully applied to three healthcare domains: prostate cancer, breast cancer, and cardiovascular disease. The generated synthetic data have been rigorously evaluated for fidelity and utility. Results show that the UMAP-based algorithm outperforms GAN- and VAE-based generation methods across different scenarios. In fidelity assessments, it achieved smaller maximum distances between the cumulative distribution functions of real and synthetic data for different attributes. In utility evaluations, the UMAP-based synthetic datasets enhanced machine learning model performance, particularly in classification tasks. In conclusion, this method represents a robust solution for generating secure, high-quality synthetic healthcare data, effectively addressing data scarcity challenges.
UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking
Authors: Rahman Md. Mahfuzur , Siddique Sunzida , Kamal Marufa , Rifat Rakib Hossain , Gupta Kishor Datta
Published: December 2024

Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains.
Disassembly of Distribution Transformers Based on Multimodal Data Recognition and Collaborative Processing
Authors: Wang Li , Chen Feng , Hu Yujia , Zheng Zhiyao , Zhang Kexin
Published: December 2024

As power system equipment gradually ages, the automated disassembly of transformers has become a critical area of research to enhance both efficiency and safety. This paper presents a transformer disassembly system designed for power systems, leveraging multimodal perception and collaborative processing. By integrating 2D images and 3D point cloud data captured by RGB-D cameras, the system enables the precise recognition and efficient disassembly of transformer covers and internal components through multimodal data fusion, deep learning models, and control technologies. The system employs an enhanced YOLOv8 model for positioning and identifying screw-fastened covers while also utilizing the STDC network for segmentation and cutting path planning of welded covers. In addition, the system captures 3D point cloud data of the transformer’s interior using multi-view RGB-D cameras and performs multimodal semantic segmentation and object detection via the ODIN model, facilitating the high-precision identification and cutting of complex components such as windings, studs, and silicon steel sheets. Experimental results show that the system achieves a recognition accuracy of 99% for both cover and internal component disassembly, with a disassembly success rate of 98%, demonstrating its high adaptability and safety in complex industrial environments.
Observer-Based Adaptive Neural Control of Quadrotor Unmanned Aerial Vehicles Subject to Model Uncertainties and External Disturbances
Authors: Mousavi Rashin , Mousavi Arash , Mousavi Yashar , Tavasoli Mahsa , Arab Aliasghar , Kucukdemiral Ibrahim Beklan , Fekih Afef
Published: December 2024

Quadrotor unmanned aerial vehicles (QUAVs) are widely recognized for their versatility and advantages across diverse applications. However, their inherent instability and underactuated dynamics pose significant challenges, particularly under external disturbances and parametric model uncertainties. This paper presents an advanced observer-based control framework to address these challenges, introducing a high-gain disturbance observer (HGDO) integrated with a neural-network-based adaptive fractional sliding mode control (NN-AFSMC) scheme. The proposed HGDO-NN-AFSMC ensures robust position and attitude tracking by effectively compensating for external disturbances and model uncertainties. A direct control approach is employed, significantly reducing computational complexity by minimizing the need for frequent online parameter updates while maintaining high tracking precision and robustness. The stability of the control system is rigorously analyzed using Lyapunov theory, and comprehensive simulation studies validate the proposed scheme’s superior performance compared to other advanced control approaches, particularly in dynamic and uncertain operational environments. The proposed HGDO-NN-AFSMC achieves a position tracking error of less than 0.03 m and an attitude tracking error below 0.02 radians, even under external disturbances and parametric uncertainties of 20%. Compared to conventional robust feedback linearization (RFBL) and nonsingular fast terminal sliding mode control (NFTSMC), the proposed method improves position tracking accuracy by 25% and reduces settling time by approximately 18%.
A Hybrid Open/Closed-Loop μ Control Method for Achieving Consistent Transient Performance in Turbofan Engines
Authors: Long Yifu , Wang Xi , Liu Jiashuai
Published: December 2024

The inconsistency in acceleration and deceleration performance between high and low altitudes is a significant challenge in aircraft engine control today. In the past, neither open-loop fuel–air ratio control nor closed-loop N-dot control could resolve this issue perfectly; the difference in acceleration and deceleration performance between high and low altitudes is even more than three times. The operational characteristics of aircraft engines vary significantly between high and low altitudes, posing challenges for transient state control in high-performance aircraft engines. To address these transient performance inconsistencies due to altitude uncertainties, a μ-synthesis adaptive tracking transition control design method with hybrid open-loop and closed-loop direct thrust control is proposed. The main innovation lies in proposing a new segmented hybrid control scheme. Under a high-power state, it employs a dual closed-loop μ-synthesis adaptive tracking framework, using fuel flow to control thrust and nozzle area to control the turbine pressure ratio. In a low-power state, a single-variable closed-loop and open-loop control architecture is applied. Simulation results show that the hybrid open/closed-loop control method can suppress the inconsistency of acceleration and deceleration performance caused by altitude uncertainties in turbofan engines, ensuring consistent robustness in acceleration and deceleration performance across different altitudes. From the ground to an altitude of 11 km, the new method has an acceleration time range of 3.44 s–3.84 s and a deceleration time range of 4.83 s–5.98 s; compared with the previous fuel–air ratio acceleration time of 4.17 s–9.12 s and deceleration time of 6.12 s–14.48 s, its high and low-altitude acceleration and deceleration consistency performance is greatly improved.
Design and Performance Optimization of a Radial Turbine Using Hydrogen Combustion Products
Authors: Su Pengfei , He Weifeng , Adam Abdalazeem , Musa Omer , Chen Wang , Lou Zeyu
Published: December 2024

The combustion of hydrogen increases the water content of the combustion products, affecting the aerodynamic performance of turbines using hydrogen as a fuel. This study aims to design a radial turbine using the differential evolution (DE) algorithm to improve its characteristics and optimize its aerodynamic performance through an orthogonal experiment and analysis of means (ANOM). The effects of varying water content in combustion products, ranging from 12% to 22%, on the performance of the radial turbine are also investigated. After optimization, the total–static efficiency of the radial turbine increased to 89.12%, which was 1.59% higher than the preliminary design. The study found that flow loss in the impeller primarily occurred at the leading edge, trailing edge, and the inlet of the suction surface tip and outlet. With a 10% increase in water content, the enthalpy dropped, Mach number increased, and turbine power increased by 4.64%, 1.71%, and 2.41%, respectively. However, the total static efficiency and mass flow rate decreased by 0.71% and 2.13%, respectively. These findings indicate that higher water content in hydrogen combustion products enhances the turbine’s output power while reducing the combustion products’ mass flow rate.
Autonomous Maneuvering Decision-Making Algorithm for Unmanned Aerial Vehicles Based on Node Clustering and Deep Deterministic Policy Gradient
Authors: Jing Xianyong , Cong Fuzhong , Huang Jichuan , Tian Chunyan , Su Zikang
Published: December 2024

Decision-making for autonomous maneuvering in dynamic, uncertain, and nonlinear environments represents a challenging frontier problem. Deep deterministic policy gradient (DDPG) is an effective method to solve such problems, but it is found that complex strategies require extensive computation and time in the learning process. To address this issue, we propose a node clustering (NC) method, inspired by grid clustering, integrated into the DDPG algorithm for the learning of complex strategies. In the NC method, the node membership degree is defined according to the specific characteristics of the maneuvering decision-making problem, and error handling strategies are designed to reduce the number of transitions in the replay database effectively, ensuring that the most typical transitions are retained. Then, combining NC and DDPG, an autonomous learning and decision-making algorithm of maneuvering is designed. The algorithm flow and the pseudo-code of the algorithm are given. Finally, the NC_DDPG algorithm is applied to a typical short-range air combat maneuvering decision problem for verification. The results show that the NC_DDPG algorithm significantly accelerates the autonomous learning and decision-making process under both balanced and disadvantageous conditions, taking only about 77% of the time required by Vector DDPG. The scale of NC impacts learning speed; the simulation results across five scales indicate that smaller clustering scales significantly increase learning time, despite a high degree of randomness. Compared with Twin Delayed DDPG (TD3), NC_DDPG consumes only 0.58% of the time of traditional TD3. After applying the NC method to TD3, NC_DDPG requires approximately 20–30% of the time of NC_TD3.
Living Lab for the Diffusion of Enabling Technologies in Agriculture: The Case of Sicily in the Mediterranean Context
Authors: Timpanaro Giuseppe , Foti Vera Teresa , Cascone Giulio , Trovato Manuela , Grasso Alessandro , Vindigni Gabriella
Published: December 2024

Enabling technologies (KETs) offer transformative potential for agriculture by addressing major challenges such as climate change, resource efficiency, and sustainable development across economic, social, and environmental dimensions. However, KET adoption is often limited by high R&D requirements, rapid innovation cycles, investment costs, and cultural or training barriers, especially among small agricultural businesses. Sicily’s agricultural sector, already strained by pandemic-related economic setbacks and inflationary pressures, faces additional barriers in adopting these technologies. To investigate these adoption challenges and develop viable solutions, the ARIA Living Lab (Agritech Research Innovation Environment) was established within the PNRR framework. A qualitative approach was used, involving documentary analysis and data from stakeholders across Sicilian agriculture. This approach enabled an in-depth exploration of sector-specific needs, infrastructure, and socio-economic factors influencing KET adoption. The analysis highlighted that adoption barriers differ significantly across sectors (citrus, olive, and wine), with public incentives and digital infrastructure playing key roles. However, a persistent lack of technical skills among farmers reduces the effectiveness of these innovations. The findings suggest that an integrated approach—combining targeted incentives, training, and enhanced infrastructure—is essential for a sustainable transition to KETs. Future research should examine collaborative efforts between farms and tech providers and evaluate the impact of public policies in promoting the widespread, informed adoption of enabling technologies.
Exploring the Feasibility of Sorghum Farming in South Africa Using Garrett’s Ranking Technique
Authors: Nkosi Zamaswazi , Marwa Nyankomo , Akinrinde Olawale Olufemi
Published: December 2024

The potential for sorghum to be the driver of economic development in Sub-Saharan economies, including South Africa, is enormous. However, there has been a notable decline in the production, use, and consumption of sorghum due to the changes in preferences and lifestyle of its consumers in Southern Africa. While existing and extant studies have studied the benefits and impacts of sorghum on the African economy, there is an inadequate understanding of the feasibility of sorghum farming in South Africa using Garrett’s ranking technique. As a result, this study explores the feasibility of sorghum farming in the Nyoni area of KwaZulu Natal, South Africa. Hence, a survey was conducted with a sample size of 150 respondents that were purposively selected. An interview schedule was also used to collect primary data, and the data were analyzed using Garrett’s ranking technique. The results of this study indicated that edaphic factors, climatic factors, volatile markets, information transfers, education, drought resistance variety, access to credit, and technological advancement were critical in making sorghum farming feasible in the Nyoni area of KwaZulu Natal. Therefore, this study recommends that there is an overarching need to explore the feasibility of sorghum farming, starting at a smaller scale until a more stable state of marketability and potential profitability has been established.
Mass Spectrometry-Based Non-Targeted Lipidome Analysis and Extraction of Markers for the Authentication of White and Black Truffle Species and Their Origin Determination
Authors: Tejedor-Calvo Eva , Marco Pedro , Fischer Markus , Creydt Marina
Published: December 2024

The visual authentication of high-value truffles (Tuber magnatum and Tuber melanosporum) is challenging, as they share similar morphological characteristics with other truffle species that have a lower economic value. This similarity complicates accurate identification and increases the risk of substitution or mislabeling, which can affect both market prices and consumer trust. For this reason, the aim of this study was to apply a non-targeted lipidomic approach using ion mobility spectrometry-mass spectrometry to distinguish between white (T. magnatum, Tuber borchii, and Tuber oligospermum) and black truffle species (T. melanosprum, T. aestivum, T. aestivum var. uncinatum, T. brumale, and T. indicum) and to determine the different geographical origins of the two most valuable truffle species (T. melanosporum and T. magnatum). Among several hundred features, 37 and 57 lipids were identified as marker compounds to distinguish white and black truffle species using MS/MS spectra and collision cross section (CCS) values, respectively. Only a few marker compounds were necessary to recognize the differences between white and black truffles. In particular, ceramides, glycerolipids, and phospholipids proved to be particularly suitable for separating the species. In addition, different metabolite profiles were determined for T. melanosporum and T. magnatum depending on their geographical origin. These findings lay the groundwork for a comprehensive quality control framework for fresh truffles, ensuring authenticity, detecting adulteration, and preserving their premium status.
Optimization of the Drying Process for Gamma-Irradiated Mushroom Slices Using Mathematical Models and Machine Learning Algorithms
Authors: Fartash Naeimi Ehsan , Khoshtaghaza Mohammad Hadi , Selvi Kemal Çağatay , Ungureanu Nicoleta , Abbasi Soleiman
Published: December 2024

Concerns over dried product quality and energy consumption have prompted researchers to explore integrated techniques for improving quality and reducing energy use. This study investigates the effect of gamma irradiation pretreatment (0, 1.2, 2.4, and 3.6 kGy) on button mushroom slices, followed by thin-layer drying at 50, 60, and 70 °C. The results indicated that increasing irradiation dose and drying temperature significantly reduced drying time. The Midilli model provided the best fıt to the drying data (R2 = 0.9969–0.9998). Artificial neural networks (ANN) accurately predicted moisture variations, achieving R2 = 0.9975 and RMSE = 0.0220. The Support Vector Machine (SVM) algorithm, employing the Pearson universal kernel in normalized mode, also performed well, with R2 = 0.9939 and RMSE = 0.0344. Similarly, in the k-nearest neighbors (kNN) algorithm with three neighbors (k = 3), the R2 and RMSE values were 0.9888 and 0.0458, respectively. Gamma irradiation enhanced the effective diffusion coefficient (Deff) to 10.796 × 10−8 m2/s, and reduced activation energy (Ea) to 11.09 kJ/mol. The highest heat utilization efficiency (41.1%) was observed at 3.6 kGy and 50 °C. These findings highlight the potential of integrating gamma irradiation pretreatment and advanced drying techniques to optimize energy use and improve the quality of dried mushroom slices.
Effects of Different Bactericides on Yellow Spot Disease of Pleurotus ostreatus
Authors: Yuan Yazhen , Fu Haoran , Lan Yufei , Chen Qiqi , Li Xinrong , Wang Hongyan
Published: December 2024

The yellow spot disease of Pleurotus ostreatus, caused by the bacterium Pseudomonas tolaasii, poses a threat to both the yield and quality of oyster mushrooms due to the limited availability of effective bactericides and the insufficient comprehensive safety assessments for the chemicals applied in the field. In this study, we performed greenhouse toxicity tests to identify highly effective bactericides against P. tolaasii, while simultaneously assessing their safety for P. ostreatus. Subsequently, we verified the field efficacy of the selected bactericide and its impact on mushroom yield through field experiments. Additionally, we established detection methods for the selected highly effective and safe bactericides in P. ostreatus and measured their residue levels during the evaluation. Our findings indicated that kasugamycin and seboctylamine acetate displayed potent inhibitory effects against P. tolaasii. The field application results showed that 1.8% seboctylamine acetate AS with concentrations of 3 mg/L exhibited good disease control, achieving 83.00%, which is comparable to the efficacy of the registered kasugamycin. Furthermore, the application of seboctylamine acetate did not adversely affect the yield of P. ostreatus, and the detected chemical residues were below the maximum residue limit. Seboctylamine acetate is anticipated to serve as a viable substitute for the currently registered fungicides, holding the promise of offering comparable or superior performance while potentially addressing concerns related to environmental impact and sustainability.
Insights into Orris (Iris pallida Lam.) In Vivo Acclimatization and Response to Salt Stress via Exogenous Melatonin Application
Authors: Meucci Annalisa , Ghelardi Cristina , Maggini Rita , Malorgio Fernando , Pezzarossa Beatrice , Rosellini Irene , Mensuali Anna
Published: December 2024

The loss of agricultural land is one of the main problems facing the global agricultural sector, and it is related to multiple phenomena; one of the main causes is soil salinization, induced both by natural processes and human activities. Among the strategies adopted to deal with soil salinization and its mitigation, the cultivation of species able to survive in saline soils seems to be an effective way of making salt-compromised lands usable. Iris pallida is a rustic plant and a species of high economic value that is mostly cultivated for perfume production. Consequently, the application of I. pallida to cover soils not suitable for crops traditionally cultivated for human and livestock nutrition could be considered; therefore, a preliminary test on the capacity of I. pallida to tolerate salinity during the acclimatization phase of micropropagated plants was conducted. Plantlets were treated with exogenous melatonin during the in vitro phase by adding it to the culture medium; therefore, during the acclimatization phase, crescent salt doses (150, 300, and 400 mM) were added to the soil every 14 days, administering melatonin to plants by a spray solution 24 h before each salt addition. At the end of the experiment, biometric measurements, chlorophylls, carotenoids, and macro-element contents were measured, and the relative water content (RWC) was determined in each salt addition. The results showed that orris plants can survive soil salt concentrations of up to 400 mM, and that the 50 µM melatonin spray treatment can protect orris rhizomes from salt side effects.
Morphological, Anatomical, Physiological and Genetic Studies of Iris aphylla L. Wild Species Conservation in “Ex Situ” Conditions
Authors: Apostol Maria , Draghia Lucia , Sîrbu Culiță , Efrose Rodica-Catalina , Flemetakis Emmanouil , Hlihor Raluca-Maria , Simion Isabela Maria , Armiñana Jose Reig , Garcia Breijo Francisco José
Published: December 2024

Wild Iris plants are usually found in spontaneous flora, but due to their ornamental characteristics, they can also be used for ornamental purposes, which means that it is very important to find the perfect conditions for plant growth. This research aimed to evaluate the ornamental value and adaptive behavior of wild Iris aphylla L. in “ex situ” conditions. Plants from wild flora were cultivated experimentally in the Floriculture field at the Faculty of Horticulture, IULS, Iași, Romania. The biometric determinations revealed the significantly higher ornamental value of conserved plants grown in “ex situ” conditions compared to wild plants. In “ex situ” conditions, the plants displayed more vigorous growth (~100%) and had a higher number of flowers per stem (5–9 flowers), whereas, in wild conditions, this species has from two to a maximum of five flowers. Given the absence of anatomical studies in the literature, detailed anatomical investigations of the leaf structure were performed, complemented by analyses of the photosynthetic pigment content to assess the plant’s physiological performance. Additionally, the molecular phylogenetic analyses conducted using two plastid markers (rbcL and trnL-F) confirmed the taxonomic classification of the native I. aphylla L. species. To the best of our knowledge, this is the first report on the molecular phylogeny of the wild Iridaceae species in Romania. These findings provide insights into the taxonomy, morphology, cultivation potential, and ornamental value of the species, supporting future conservation and horticulture development programs.
Effect of Fertilizer, Sowing Date, and Seeding Rate on Biomass and Yield of Pea (Pisum savitum L.) Grown Under Dry Steppe and Steppe Conditions
Authors: Amantayev Bekzak , Shelia Vakhtang , Kipshakbayeva Gulden , Shestakova Nina , Gordeyeva Yelena , Kulzhabayev Yeldos , Zhanbyrshina Nursaule , Lutschak Paul , Absattarova Aiman , Kurishbayev Akhylbek , Hoogenboom Gerrit
Published: December 2024

The impact of different agronomic practices on pea adaptability to terminal drought conditions can provide increased knowledge on optimizing pea yield, biomass, and environmental footprints. Two field experiments in the layout of the split–split plot and a 3-factor (fertilizer × sowing date × seeding rate) design were carried out on pea crops in 2021 and 2022 in Kazakhstan’s dry steppe and steppe zones. The objective was to evaluate the significance of these factors and their interactions on biomass and yield based on 12 treatments in the dry steppe and 18 treatments in the steppe. In both zones, fertilizer effect on biomass and yield was significant (p < 0.05) and resulted in a biomass increase of 17% and a yield increase of 16% in the dry steppe and 19% and 17.9%, respectively, in the steppe. The sowing date’s effect on biomass and yield in both zones was also significant, with maximum yield with late sowing (20 May) and biomass increased by 10% compared to the earliest sowing date (10 May) and yield increase of 9.2% in the dry steppe, and 15.7%, and 30%, respectively, in the steppe. Seeding rate and none of the first- and second-order interactions between these three factors on biomass and yield were significant in either zone. The relationship between factors and final biomass and yield showed that fertilizer application was dominant. Our research also showed that yield was highly correlated (r = 0.8–1.0, p < 0.05) with biometric indicators of plants, such as the weight of seeds per 1 m2 and the weight of seeds per plant. The findings from this study indicate that adaptive crop production to increase the yield of peas can be used for environmental conditions of dry steppe and steppe based on the development of new agronomic practices, especially those that include fertilizer application with a combination of sowing dates.
Efficiency of 3,4-Dimethylpyrazole Phosphate in Mitigating N2O Emission Varied with Irrigation Regime in Drip-Irrigated Wheat Field
Authors: Liang Yueping , Zhang Yingying , Liu Tianyu , Si Zhuanyun , Gao Yang
Published: December 2024

Agricultural soils are major anthropogenic sources of N2O emissions. The application of nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP) has been proved to be an effective management measure to mitigate N2O emissions. However, the influence mechanism of DMPP on the mitigation of soil N2O emissions under different irrigation regimes remains unclear. Therefore, a lysimeter experiment was conducted to study the effects of irrigation level (lower irrigation limits of 75%, 65%, and 55% of field capacity (FC), signed as WH, WM, and WL) and DMPP addition (0% and 1% of N application, signed as D0 and D1) on N2O emissions, soil environmental factors such as ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), water-filled pore space (WFPS), soil temperature, and the abundances of N2O-related genes (AOA amoA, AOB amoA, nirS, and nirK). The results showed that soil N2O emissions increased with the increasing of irrigation level. The efficiency of DMPP mitigating N2O emissions varies depending on irrigation regime. Compared to D0, D1 strongly decreased cumulative N2O emissions by 11.27%, 18.96%, and 15.05% in the WL, WM, and WH conditions, respectively. Meanwhile, D1 caused an obvious reduction in the AOB amoA gene by 29.73%, 47.02%, and 22.41%, respectively, but there was no significant effect on the AOA amoA gene. D1 was effective in decreasing nirS and nirK genes except in the WL condition; the percentages of reduction were 48.45%, 40.84% and 37.18%, 44.97% in the WM and WH conditions, respectively. In addition, D1 caused an increase in NH4+-N content and a decrease in NO3−-N content, WFPS, and soil temperature in all irrigation regimes. A higher significant correlation was observed between N2O emissions and NH4+-N and AOB amoA in the WL and WM conditions, while a significant correlation was observed between N2O emissions and NO3−-N, nirK, and nirS in the WH condition. It was revealed that with the increase in irrigation level, the main source of N2O emissions might change from nitrification to denitrification. Overall, our study indicated that in the WL and WM conditions, the mitigation of N2O emissions by DMPP was primarily attributable to the inhibition of the AOB amoA gene, whereas the inhibition of nirS and nirK genes was likely the dominant mechanism in the WH condition. The findings of this study will provide a theoretical basis for the application of a nitrification inhibitor for drip-irrigated winter wheat fields in the North China Plain.