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Understanding the correlation between soil chemical properties and tea quality is essential for the comprehensive management of ancient tea gardens. However, the specific links between these factors in ancient tea gardens remain underexplored. This study analyzes the soil chemical properties of four distinct research regions in Nanhua County to explore their effects on key chemical components in ancient tea garden teas, providing a scientific basis for improving the quality of ancient tea garden teas through soil management. Employing high performance liquid chromatography (HPLC) and inductively coupled plasma mass spectrometry (ICP-MS), the chemical components of tea and the chemical properties of the soil were meticulously quantified. Following these measurements, the integrated fertility index (IFI) and the potential ecological risk index (PERI) were evaluated and correlation analysis was conducted. The results revealed that ancient tea garden tea quality is closely linked to soil chemical properties. Soil’s total nitrogen (TN), total sulfur (TS), and available potassium (AK) negatively correlate with tea’s catechin gallate (CG) component and AK also with polyphenols. Most other soil properties show positive correlations with tea components. The research also evaluated soil heavy metals’ IFI and PERI. IFI varied significantly among regions. Hg’s high pollution index indicates ecological risks; Cd in Xiaochun (XC) region poses a moderate risk. PERI suggests moderate risk for XC and Banpo (BP), with other areas classified as low risk. Implementing reasonable fertilization and soil amelioration measures to enhance soil fertility and ensure adequate supply of key nutrients will improve the quality of ancient tea gardens. At the same time, soil management measures should effectively control heavy metal pollution to ensure the quality and safety of tea products. Insights from this study are crucial for optimizing soil management in ancient tea gardens, potentially improving tea quality and sustainability. Full article

Monitoring of operations has become a critical activity in forestry, aiming to provide the data required by planning and production management. Conventional methods, on the other hand, come at a high expense of resources. A neural network was trained, validated, and tested in this study based on multi-modal data to classify relevant operational events in mechanized weed control operations. The architecture of a neural network was tuned in terms of the number of hidden layers and neurons, and the regularization term was set at various values to obtain optimally tuned models for three data modalities: triaxial acceleration data coupled with speed extracted from GNSS signals (AS), triaxial acceleration (A), and speed alone (S). In the training and validation phase, the models based on AS and A achieved a very high classification accuracy, accounting for 92 to 93% when considering four relevant events. In the testing phase, which was run on unseen data, the classification accuracy reached figures of 91 to 92%, indicating a good generalization ability of the models. The results point out that multimodal data are able to provide the features for distinguishing events and add spatial context to the monitored operations, standing as a suitable solution for offline, partly automated monitoring. Future studies are required to see how the capabilities of online, real-time technologies such as deep learning coupled with computer vision can add more context and improve classification performance. Full article

The exploration and development of oil and gas resources in deep formations is a key strategic priority for national energy production. However, manual methods for handling gas kicks suffer from low operating accuracy and inefficiency during high-temperature and high-pressure deep well drilling. To address the need for real-time bottomhole pressure prediction and control, an efficient gas–liquid–solid computing model was developed based on the gas slip model and cuttings settling velocity model. By integrating this model with an automatic choke adjustment system, an automatic gas kick attenuation model for deep well drilling was established. Results show that, compared to the driller’s and wait-and-weight methods, the automatic gas kick attenuation method significantly reduces peak choke pressure due to its larger frictional pressure drop and higher cuttings hydrostatic pressure. The automatic attenuation method not only leads to an average reduction of 28.42% in maximum choke/casing pressure but also accelerates gas removal, achieving gas kick attenuation ten times faster than the driller’s method and seven times faster than the wait-and-weight method. The study also investigates the influence of gas solubility, well depth, gas influx volume, formation permeability, and drilling fluid volumetric flow rate on gas kick attenuation characteristics. The findings provide a solid foundation for improving the efficiency of gas kick management in deep well drilling operations. Full article

The use of non-degradable plastic mulch has become an essential agricultural practice for increasing crop yields, but continued use has led to contamination problems and in some cropping areas decreases in agricultural productivity. The subsequent emergence of biodegradable plastic mulches is a technological solution to these issues, so it is important to understand how different soil characteristics and field management strategies will affect the rate at which these new materials degrade in nature. In this work, a series of lab-scale hydrolytic degradation experiments were conducted to determine how different soil characteristics (type, pH, microbial community composition, and particle size) affected the degradation rate of a sprayable polyester–urethane–urea (PEUU) developed as a biodegradable mulch. The laboratory experiments were coupled with long-term, outdoor, soil degradation studies, carried out in Clayton, Victoria, to build a picture of important factors that can control the rate of PEUU degradation. It was found that temperature and acidity were the most important factors, with increasing temperature and decreasing pH leading to faster degradation. Other important factors affecting the rate of degradation were the composition of the soil microbial community, the mass loading of PEUU on soil, and the degree to which the PEUU was in contact with the soil. Full article

Mitigation of greenhouse gases (GHGs), improving nutrient-use efficiency (NUE), and maximizing yield in rainfed lowland rice cultivation poses significant challenges. To address this, a two-year field experiment (2020 and 2021) was conducted in Assam, India, to examine the impact of different fertilizer-management practices on grain yield, NUE, and GHGs in wet direct-seeded rice (Wet-DSR) during the kharif season. The experiment included eight treatments: control; farmer’s practice (30-18.4-36 kg N-P₂O₅-K₂O ha⁻¹); state recommended dose of fertilizer (RDF) @ 60-20-40 kg N-P₂O₅-K₂O ha⁻¹ with N applied in three splits @ 30-15-15 kg ha⁻¹ as basal, at active tillering (AT), and panicle initiation (PI); best fertilizer management practices (BMPs): 60-20-40 kg N-P₂O₅-K₂O ha⁻¹ with N applied in three equal splits as basal, at AT, and PI; and fertilizer deep placement (FDP) of 120%, 100%, 80%, and 60% N combined with 100% PK of RDF. The experiment was arranged out in a randomized complete block design with three replications for each treatment. The highest grain yield (4933 kg ha⁻¹) and straw yield (6520 kg ha⁻¹) were achieved with the deep placement of 120% N + 100% PK of RDF. FDP with 80% N + 100% PK reduced 38% N₂O emissions compared to AAU’s RDF and BMPs, where fertilizer was broadcasted. This is mainly due to the lower dose of nitrogen fertilizer and the application of fertilizer in a reduced zone of soil. When considering both productivity and environmental impact, applying 80% N with 100% PK through FDP was identified as the most effective practice. Full article

Development of native microbial consortia is crucial for the sustainable management of plant diseases in modern agriculture. This study aimed to evaluate the antagonistic potential of various microbial isolates against Rhizoctonia bataticola, a significant soil-borne pathogen. A total of 480 bacteria, 283 fungi, and 150 actinomycetes were isolated and screened using in vitro dual plate assays. Among these, isolates 5F, 131B, 223B, and 236B demonstrated the highest antagonistic activity, with inhibition rates of 88.24%, 87.5%, 81.25%, and 81.25%, respectively. The selected isolates were further assessed for abiotic stress tolerance, revealing their ability to thrive under extreme conditions. Characterization of biocontrol and plant growth-promoting activities revealed the production of siderophores, hydrogen cyanide, ammonia, chitinase, and indole-3-acetic acid, along with the solubilization of zinc and phosphorus. Compatibility tests confirmed the potential of forming effective microbial consortia, which significantly reduced the percent disease index in cluster bean. The most effective consortium, comprising Trichoderma afroharzianum 5F, Pseudomonas fluorescens 131B, Bacillus licheniformis 223B, and Bacillus subtilis 236B, achieved a 76.5% disease control. Additionally, this consortium enhanced total phenol (92.1%), flavonoids (141.6%), and antioxidant defense enzyme activities including POX (188.5%), PPOX (116.3%), PAL (71.2%), and TAL (129.9%) in cluster bean plants over the infected control, leading to substantial improvements in systemic resistance of plants. This consortium also significantly enhanced plant height, fresh weight, dry weight, number of pods per plant, and seed yield over the infected control as well as mock control. This study underscores the potential of these robust microbial consortia as a sustainable and effective strategy for managing R. bataticola and enhancing crop productivity under extreme environmental conditions. Full article

In a rapidly evolving technological landscape, manufacturers are increasingly pressured to undertake digital transformation, with smart manufacturing serving as a crucial milestone in this process. This study investigates the key factors influencing the implementation of smart manufacturing from a supply chain perspective, employing the analytical hierarchy process (AHP) to analyze collected data from senior managers of manufacturing firms. The findings highlight several critical factors, including the commitment of senior executives, the recruitment of skilled professionals, interdepartmental collaboration, and financial support. Moreover, this study reveals differing priorities between large and small manufacturers: large firms emphasize the importance of the Industrial Internet of Things (IIoT), while smaller firms prioritize understanding end-consumer needs and product trends. This study emphasizes that smart manufacturing is not only for optimizing the operational efficiency of manufacturing firms but also for supporting sustainability efforts through more effective use of resources and reduced environmental impact of work processes. Full article

Annually, the global plastic waste generation adds up to over 353 million tonnes, which is associated with substantial environmental and societal issues, such as microplastic pollution and landfill management. Despite many attempts to integrate sustainable circular economy strategies into the plastic industry, several challenges have resulted in material loss and poor-quality recycled products. To address these challenges, this study proposes a material traceability system to overcome the issue of flawed recycling of plastic. The authors employed a systematic literature review and meta-analysis to summarise the current state of traceability in the plastic recycling industry. The results revealed that blockchain technology is the most promising framework amongst various traceability systems; however, its implementation is hindered for three reasons. First, future systems must prioritise interoperability to ensure seamless integration; second, standardisation is imperative for effective traceability; and third, implementing digital and physical traceability is essential to maximise the value of materials by enabling improved material identification and enhancing sorting efficiency. Further, it emerged that integrating quality control into traceability solutions is essential for improved recycled content in plastic products. By shedding light on these insights, this study contributes to developing traceability systems in the plastic recycling industry, guiding policymakers, industry practitioners, and researchers alike. Ultimately, the implementation of effective traceability mechanisms has the potential to drive plastic circularity by improving material identification, sorting practices, and overall transparency within the industry. Full article

Studies were initiated to find new effective fungicides to use under field conditions to discover novel approaches for optimizing disease management in sugar beet crops. Cercospora leaf spot (CLS), a prevalent foliar disease in sugar beet crops worldwide, is caused by the fungal pathogen Cercospora beticola Sacc. This disease has become the most prevalent pathogen in sugar beet crops across nearly all European growing regions, including Hungary. The epidemic spread of this disease can cause up to 50% yield loss. The use of fungicides has been a cornerstone in managing CLS of sugar beet due to the limited efficacy of non-chemical alternatives. However, the emergence of fungicide-resistant strains of Cercospora beticola Sacc. in recent decades has compromised the effectiveness of certain fungicides, particularly those belonging to the QoI (FRAC Group 11) and DMI (FRAC Group 3) classes. Hungary is among the many countries where resistance to these fungicides has developed due to their frequent application. Picolinamides represent a novel class of fungal respiration inhibitors targeting Complex III within the Quinoine-Inside Inhibitor (QiI) group. Two innovative fungicides from this class, fenpicoxamid and florylpicoxamid (both classified under FRAC Group 21), were evaluated for their efficacy in managing CLS of sugar beet in Hungary during the 2020 and 2021 growing seasons. Both fungicides were applied as formulated products at various application rates and demonstrated superior efficacy in controlling CLS compared to untreated control plots and the reference fungicides difenoconazole and epoxiconazole. The results consistently demonstrated that all tested application rates of fenpicoxamid and florylpicoxamid effectively controlled CLS in sugar beet, exhibiting a clear dose–response relationship. Disease severity, as measured by the area under the disease progress curve (AUDPC), was significantly correlated with yield reduction but showed no significant association with root sugar content. Moreover, data from both study years indicated that picolinamide fungicides applied at a rate of 75 g ai/ha significantly outperformed difenoconazole (100 g ai/ha) in controlling the CLS of sugar beet. Additionally, higher application rates of picolinamides at 100–150 g ai/ha outperformed epoxiconazole at 125 g ai/ha in disease suppression. Fenpicoxamid is currently registered for use in cereals within Europe, and outside of Europe in Banana against Black Sigatoka (eff. Mycosphaerella fijiensis). Florylpicoxamid, while not yet registered in Europe, is undergoing approval processes in various countries worldwide for a range of crops and is continually being evaluated for potential market introduction. Additional details regarding the efficacy of florylpicoxamid against CLS in sugar beet were presented at ‘The 10th International Conference on Agricultural and Biological Sciences (ABS 2024, Győr-Hungary)’ in 2024. Full article

This study investigates gender-based differences in the gut microbiota of Simmental cattle, focusing on bacterial, archaeal, and fungal communities. Fecal samples were collected and analyzed using high-throughput sequencing, with taxonomic classification performed through the SILVA and UNITE databases. Alpha and beta diversity metrics were assessed, revealing significant differences in the diversity and composition of archaeal communities between males and females. Notably, females exhibited higher alpha diversity in archaea, while beta diversity analyses indicated distinct clustering of bacterial and archaeal communities by gender. The study also identified correlations between specific microbial taxa and hematological parameters, with Treponema and Methanosphaera showing gender-specific associations that may influence cattle health and productivity. These findings highlight the importance of considering gender in microbiota-related research and suggest that gender-specific management strategies could optimize livestock performance. Future research should explore the role of sex hormones in shaping these microbial differences. Full article

Targeted radionuclide therapy (TRT) for internal pathway-directed treatment is a game changer for precision medicine. TRT improves tumor control while minimizing damage to healthy tissue and extends the survival for patients with cancer. The application of theranostic-paired TRT along with cellular phenotype and genotype correlative analysis has the potential for malignant disease management. Chelation chemistry is essential for the development of theranostic-paired radiopharmaceuticals for TRT. Among image-guided TRT, ⁶⁸Ga and ^99mTc are the current standards for diagnostic radionuclides, while ¹⁷⁷Lu and ²²⁵Ac have shown great promise for β- and α-TRT, respectively. Their long half-lives, potent radiobiology, favorable decay schemes, and ability to form stable chelation conjugates make them ideal for both manufacturing and clinical use. The current challenges include optimizing radionuclide production processes, coordinating chelation chemistry stability of theranostic-paired isotopes to reduce free daughters [this pertains to ²²⁵Ac daughters ²²¹Fr and ²¹³Bi]-induced tissue toxicity, and improving the modeling of micro dosimetry to refine dose–response evaluation. The empirical approach to TRT delivery is based on standard radionuclide administered activity levels, although clinical trials have revealed inconsistent outcomes and normal-tissue toxicities despite equivalent administered activities. This review presents the latest optimization methods for chelation-based theranostic radiopharmaceuticals, advancements in micro-dosimetry, and SPECT/CT technologies for quantifying whole-body uptake and monitoring therapeutic response as well as cytogenetic correlative analyses. Full article

Technological progress has driven the integration of new technologies in the field of industrial automation, but a structured framework is often lacking to efficiently guide the transition from traditional industries. This article presents the implementation of advanced technologies on FESTO’s (MPS-500) modular production system, using the reference architectural model for Industry 4.0 (RAMI 4.0) as a guide for scaling. It highlights the importance of the synergy between information technologies (ITs), which enables the development of a multi-level processing system. This system performs concurrent tasks, thus managing execution and manufacturing through an MES based on requests from the cloud. On the other hand, at a lower level, a fog computing system was integrated, which relieves the processing load by distributing processes locally. In addition, matrix mapping was performed to map the integrated technologies within the context of a reference model, allowing a clear alignment between the different levels of the system. The results show a significant reduction in waiting times between batches and operations, which directly improves productivity and offers greater flexibility, that is crucial for SMEs during their growth and scaling process towards Industry 4.0. Full article

Biochar amendment and substituting chemical fertilizers with organic manure (organic substitution) have been widely reported to improve intensive vegetable production. However, considering its high potential for reducing carbon and reactive nitrogen (Nr) footprints, very few comprehensive evaluations have been performed on the environmental and economic aspects of biochar amendment or organic substitution. In this study, the comprehensive environmental damage costs from carbon and Nr footprints, measured using the life cycle assessment (LCA) methodology, followed a cradle-to-gate approach, and the carbon storage benefits were incorporated into the newly constructed net ecosystem economic benefit (NEEB) assessment frame in addition to the conventional product income–input cost-benefit methods. One kilogram of harvested vegetables for carbon/Nr footprints and one hectare of cultivated land per crop for cost and benefit were adopted as functional units considering the multi-cropping characteristics for intensive vegetable production. Five fertilization treatments were included: no fertilizer (CK); synthetic fertilizer application (SN); biochar amendment (NB); organic substitution (NM); and a combination of biochar and organic substitution (NMB). These were investigated for five consecutive years of vegetable crop rotations in a typically intensified vegetable production region in China. Adopting the revised NEEB methodology, NB significantly reduced carbon footprint by 73.0% compared to no biochar addition treatment. Meanwhile, NB significantly increased the total benefits by 9.7% and reduced the environmental damages by 52.7% compared to NM, generating the highest NEEB, making it the most effective fertilization strategy among all treatments. It was 4.3% higher compared to NM, which was not significant, but significantly higher than SN and NMB, by 23.0% and 13.6%, respectively. This finding highlights the importance of considering carbon storage benefit for properly assessing NEEB, which is important for developing effective agricultural management strategies and promoting intensive vegetable production with a more sustainable approach. Full article

The speedy progress of computer vision and machine learning engineering has inaugurated novel means for improving the purchasing experiment in brick-and-mortar stores. This paper examines the utilization of YOLOv (You Only Look Once) and DeepSORT (Deep Simple Online and Real-Time Tracking) algorithms for the real-time detection and analysis of the purchasing penchant in brick-and-mortar market surroundings. By leveraging these algorithms, stores can track customer behavior, identify popular products, and monitor high-traffic areas, enabling businesses to adapt quickly to customer preferences and optimize store layout and inventory management. The methodology involves the integration of YOLOv5 for accurate and rapid object detection combined with DeepSORT for the effective tracking of customer movements and interactions with products. Information collected in in-store cameras and sensors is handled to detect tendencies in customer behavior, like repeatedly inspected products, periods expended in specific intervals, and product handling. The results indicate a modest improvement in customer engagement, with conversion rates increasing by approximately 3 percentage points, and a decline in inventory waste levels, from 88% to 75%, after system implementation. This study provides essential insights into the further integration of algorithm technology in physical retail locations and demonstrates the revolutionary potential of real-time behavior tracking in the retail industry. This research determines the foundation for future developments in functional strategies and customer experience optimization by offering a solid framework for creating intelligent retail systems. Full article

Accurately predicting the wheat potential yield (PY) is crucial for enhancing agricultural management and improving resilience to climate change. However, most existing crop models for wheat PY rely on type-specific parameters that describe wheat traits, which often require calibration and, in turn, reduce prediction confidence when applied across different spatial or temporal scales. In this study, we integrated eco-evolutionary optimality (EEO) principles with a universal productivity model, the Pmodel, to propose a comprehensive full-chain method for predicting wheat PY. Using this approach, we forecasted wheat PY across China under typical shared socioeconomic pathways (SSPs). Our findings highlight the following: (1) Incorporating EEO theory improves PY prediction performance compared to current parameter-based crop models. (2) In the absence of phenological responses, rising atmospheric CO₂ concentrations universally benefit wheat growth and PY, while increasing temperatures have predominantly negative effects across most regions. (3) Warmer temperatures expand the window for selecting sowing dates, leading to a national trend toward earlier sowing. (4) By simultaneously considering climate impacts on wheat growth and sowing dates, we predict that PY in China’s main producing regions will significantly increase from 2020 to 2060 and remain stable under SSP126. However, under SSP370, while there is no significant trend in PY during 2020–2060, increases are expected thereafter. These results provide valuable insights for policymakers navigating the complexities of climate change and optimizing wheat production to ensure food security. Full article