Complete Design Research Articles

Perceptions of a Digital Dental Technology Curriculum: A Qualitative Study of Dental Technology Students.

Integrating digital dental technology into undergraduate curricula can better prepare dental technology students for a digitally driven workplace. However, students' perspectives on this education are sparse in the literature. This qualitative study explored dental technology students' perceptions of a digital dental technology curriculum. An online questionnaire was administered to third-year and fourth-year students, as well as recent graduates from the West China School of Stomatology. It comprised three sections: (1) demographic data, (2) perceptions of learning effectiveness and its impact, and (3) suggestions for curriculum improvements. Data were analysed using descriptive statistics and a chi-squared test. Sixty-nine valid responses were collected, yielding an effective response rate of 90.79%. Over 80% of respondents thought the courses beneficial for professional learning, career selection and practical work. However, 35% of third-year students reported being unable to complete digital design independently upon finishing the courses, and 62.50% of fourth-year students also lacked confidence in this area. While 95% of third-year students and 87.5% of fourth-year students intended to pursue digital-related positions, only 33.33% of employed graduates were engaged in relevant professions, indicating that the demands for digital competence in the workplace significantly exceeded the curriculum content. This study found that most students had a positive perception of the digital dental technology curriculum. However, a gap remains between curriculum content and professional practice demands. Further research and curriculum shifts driven by skill application and work scenarios are required to support the digital transformation of dental technology education and dentistry.

Gulf of Mexico Case Study Highlights Smart-Completion Application

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218794, “Multifaceted Smart Completion Application: A Gulf of Mexico Case Study,” by Jeffrey P. Manning, Ben J. Carter, SPE, and Leila Bannister, Occidental Petroleum, et al. The paper has not been peer reviewed. _ Reservoir heterogeneity, stacked pay systems, reservoir compartmentalization, and high temperature and pressure are common challenges seen in Gulf of Mexico (GOM) fields. The use of conventional completion design in vertical wells with commingled production from multiple zones has led to multiple operational challenges. In the complete paper, the authors explore the use of intelligent completions in deepwater GOM as a solution. Benefits of Intelligent Completions Intelligent completions allow collection, transmission, and validation of real-time, high-frequency data. This enables analysis of completion, production, and reservoir data while selected reservoir zones are controlled and monitored remotely in real time. Intelligent completions such as multiphase flowmeters, bottomhole pressure gauges, and cased-hole logging tools enable real-time data gathering and monitoring. Additionally, downhole flow-control systems, pinpoint stimulation, intelligent injection, and multizonal isolation technology are examples of intelligent completions that enable production optimization. This study concentrates on the application of downhole intelligent choke systems in wells that use radio frequency identification technology. Combining this technology with smart sensors or Global Positioning System technology enables sensor data to be transmitted wirelessly. Operations such as opening and closing of valves, indexing and release mechanisms, and a variety of other functions can be implemented using this technology. Multiple tools can be run at the same time without compromising the tool’s internal diameter. Each tool has a unique address; therefore, tags can be coded with data that identifies an individual tool to be actuated. Intelligent completions can be broadly categorized into the following: - Downhole flow-control systems: These are passive or active control systems to control flow from each zone. They are remotely controlled by hydraulic lines from the surface facility. - Permanent downhole sensors: These are discrete or distributed systems that monitor downhole parameters. - Permanent downhole chemical injection: These systems allow for treatment at different points in the wellbore and flowlines to prevent issues caused by corrosion, hydrates, asphaltenes, paraffin, and scaling. Case Study: Practical Use of Intelligent Completions in the GOM The operated field in the northern GOM has successfully used intelligent completions in multiple vertical producing wells to help significantly reduce operating costs associated with topsides water processing, mitigate the risk of sand production, and allow for better subsurface understanding using individual zone-flow tests (this synopsis concentrates on the first two of these three aspects). Intelligent completions enabled these operations without the use of an intervention vessel.

How ADNOC Opened the UAE’s Pre-Khuff Offshore Gas Play Using Advanced Fracturing Tech

A hidden gem may lie beneath one of the world’s latest megaprojects. Next year, the Abu Dhabi National Oil Company (ADNOC) is set to launch production from its largest-ever gas development within the UAE’s shallow-water Ghasha concession. Located some 190 km northwest of Abu Dhabi, the first phase will see gas flow from the Dalma field, followed by production from the Hail and Ghasha fields, with combined output expected to boost the UAE domestic gas supply by 1.5 Bcf/D by decade’s end. Set aside for several decades due to high levels of H2S and CO2, ADNOC is now pushing to make natural gas a bigger part of its energy strategy and has thus proudly positioned this multibillion-dollar project as the world’s largest sour-gas development. As operator, ADNOC holds a 70% stake in the concession alongside partners Eni (10%), PTTEP (10%), OMV (5%), and Lukoil (5%). ADNOC has also committed to making Hail and Ghasha the first major gas fields to operate with net-zero emissions, supported by substantial investments in carbon capture technology and the use of nuclear power. Yet there may be more to Ghasha’s supply-side potential, thanks to a team of engineers and subsurface experts who weren’t ready to give up on a stubborn rock. Discovered in 1979, the Pre-Khuff formation is a gas-rich sandstone that, though free from H2S, presents other challenges that have so far kept it from reaching commercialization. “The Pre-Khuff has performed well across the region—in Saudi Arabia and Qatar—but here in the UAE, we’ve faced 4 decades of unsuccessful attempts to unlock its potential,” said Jasim Ali Alloghani, ADNOC’s subsurface manager for the Gasha concession. He explained the complexities of the Pre-Khuff formation center around its depth, temperature, and tightness. Offshore the UAE, the Pre-Khuff formation is found 16,000 ft below the mudline where temperatures are more than 350°F and reservoir pressures exceed 10,000 psi. The sandstone is also rich in clay, with estimated clay content ranging from 10 to 40%. This leaves the rock with an upper porosity of about 12%, and a permeability range of 0.01 to 1 mD. The difficulties associated with the Pre-Khuff formation intensify from there. With a Young’s modulus of around 10 million psi, the rock is more than twice as stiff as much of the prolific Wolfcamp Shale in the Permian Basin that spans Texas and New Mexico in the US. The formation is also dominated by natural fractures and varying fracture gradients. Additionally, wellbore washouts while drilling in the deep reservoir have hindered geomechanical data acquisition. ADNOC has also found that the state of stress changes both laterally and vertically, i.e., anisotropy, due to the reservoir’s structural deformation. Additionally, ADNOC has faced challenges with designing effective completion designs due to limited technology and expertise. However, ADNOC’s fortunes with the Pre-Khuff shifted in early 2023 when the company executed the first successful multistage hydraulic fracturing treatments ever performed offshore Abu Dhabi. ADNOC reported a five- to 10-fold increase in initial flow rates from Pre-Khuff wells compared with initial attempts.

Stress shadow effects in multistage horizontal hydrofracturing of tight reservoirs: a numerical analysis considering perforation cluster spacings and fracturing sequences

AbstractMultistage horizontal fracturing technology of reservoirs has been widely used to enhance tight hydrocarbon resource recovery. Determining the proper perforation cluster spacing is crucial to developing a complex fracture network that connects natural rock fractures and facilitates gas flow in reservoirs. The stress shadow effect that occurs between multiple clusters has a significant effect on the development of the fracture network in reservoirs. The quantification of the stress shadow effect and its influences on the development of fracture networks has not been resolved satisfactorily due to experimental and numerical difficulties with detecting and identifying crack propagation and intersection in deep reservoirs. In this study, we used an adaptive finite element-discrete element method to analyze crack propagation and intersection in tight shale reservoirs by altering perforation spacings and fracture sequences. The effects of five perforation cluster spacings using sequential, simultaneous and parallel fracturing techniques were compared. The non-linear properties of shales, hydromechanical coupling and fluid leak-off effects, proppant transport, dual-rupture criteria of strength and energy, and intersection of hydraulic fractures were taken into account in the simulation. The areas affected by the stress shadow and the optimum perforation cluster spacing in terms of hydraulic fracture propagation lengths, volumes, and microseismic magnitudes were evaluated when adopting different fracturing methods. This study extends the understanding of the impact of the different independent factors that contribute to the stress shadow effect and, therefore, provides new information to help guide wellbore completion design in horizontal fracturing wells.

Influence of different lead concentrations on the growth parameters of French marigold (Tagetes patula L.)

This study was carried out to investigate the effect of different lead (Pb) concentrations on some growth characteristics of French marigold (Tagetes patula L.). The research was carried out at Siirt University (Turkey) Faculty of Agriculture, Agricultural Biotechnology Laboratory. In the study, French marigold (T. patula L.) seeds were used as plant material. Five different concentrations of lead (0, 200, 400, 600, and 800 ppm) were considered as research subjects. 7 pots were used for each concentration, and the laboratory experiment was set up in a randomized complete plots design with 7 replications. In the experiment, a 2:2:1 ratio peat:sand: soil mixture was used as the plant growing medium. Plant height (cm), stem thickness (mm), the number of branches per plant, the number of flowers per plant, single flower weight, and plant fresh and dry weights (g) were evaluated. According to the results, the difference between Pb concentrations was found to be significant in terms of all parameters except for the number of branches, and fresh and dry plant weights. In the study, according to Pb concentrations, plant height values were between 46.25-52.50 cm, stem thickness was between 4.13-5.77 mm, the number of branches was between 4.25-5.75 per plant, the number of flowers was between 3.25-6.25 per plant, single flower weight was between 0.84-1.49 g, plant fresh weight was between 11.66-14.32 g and plant dry weight varied between 1.10-1.29 g. In the study, promising results were obtained that the French marigold can be used for phytoremediation in Pb-contaminated areas.

Gas–Water–Sand Inflow Patterns and Completion Optimization in Hydrate Wells with Different Sand Control Completions

Sand production poses a significant problem for marine natural gas hydrate efficient production. However, the bottom hole gas–water–sand inflow pattern remains unclear, hindering the design of standalone screen and gravel packing sand control completions. Therefore, gas–water–sand inflow patterns were studied in horizontal and vertical wells with the two completions. The experimental results showed that gas–water stratification occurred in horizontal and vertical standalone screen wells. The gas–water interface changed dynamically, leading to an uneven screen plugging, with severe plugging at the bottom and high permeability at the top. The high sand production rate and low well deviation angle exacerbated screen plugging, resulting in a faster rising rate of the gas–water interface. The screen plugging degree initially decreased and then increased as the gas–water ratio increased, resulting in the corresponding variation in the gas–water interface rising rate. Conversely, gas–water stratification did not occur in the gravel packing well because of the pore throat formed between the packing gravels. However, the impact of gas and water led to gravel rearrangement and the formation of erosion holes, causing sand control failure. A higher gas–water ratio and lower packing degree could result in more severe destabilization. Therefore, for the standalone screen completion, sand control accuracy should be designed at different levels according to the uneven plugging degree of the screen. For the gravel packing completion, increase the gravel density without destabilizing the hydrate reservoir, and use the coated gravel with a cementing effect to improve the gravel layer stability. In addition, the screen sand control accuracy inside the gravel packing layer should be designed according to the sand size to keep long-term stable hydrate production.

Vortex-enhanced jet impingement and the role of impulse generation rate in heat removal using additively manufactured synthetic jet devices

This article presents an approach to the design and fabrication of synthetic jet devices (SJDs) using rapid prototyping via additive manufacturing, marking the first study to employ this method for such devices. This manufacturing technique empowers researchers with complete design freedom, enabling the production of ultra-thin SJDs—as thin as 4 mm—without mechanical fasteners and facilitating the rapid fabrication of multiple devices with varying geometries. To showcase the potential of this method, SJDs with conical and cylindrical cavities and orifices ranging from 1.6 mm to 7 mm were designed, fabricated, and tested.These devices achieved air jet exit velocities exceeding 106 m/s using a single piezoelectric diaphragm—among the highest reported in the literature—validating the effectiveness of this manufacturing approach. This high jet velocity is significant for practical applications requiring efficient thermal management, such as cooling high-power-density electronics, where compact and energy-efficient solutions are essential. Beyond achieving high velocities, it was revealed that maximizing jet velocity alone is not always optimal for heat removal. The hydrodynamic impulse generation rate was introduced as a more significant factor influencing heat transfer performance. By fabricating and testing multiple SJDs with different geometries, it was demonstrated that the impulse generation rate, which accounts for both jet velocity and flow rate, better correlates with enhanced heat transfer capabilities than jet velocity alone. This insight addresses an often-overlooked parameter in SJD design and has substantial implications for optimizing heat removal performance. Moreover, lumped element modeling, tuned solely on diaphragm deflection behavior, accurately predicted device performance and was validated using a hotwire anemometer. This model effectively characterizes center-axis orifice devices and confirms its applicability to thin-cavity designs, providing a valuable tool for future SJD development. Despite moderate volume flow rates (0.2 to 0.8 m3/h), the fabricated SJDs delivered significant improvements in heat transfer. Compared to natural convection, these devices achieved over 13 times greater heat removal rates, with an average heat transfer coefficient exceeding 120 W/m2·K over a 30 mm × 30 mm heated surface. These findings demonstrate the practicality and effectiveness of vortex-enhanced synthetic jet impingement for targeted and efficient cooling of localized hot spots. This approach offers multiple advantages over traditional rotary cooling systems like fans, including increased reliability, lower profile, while consuming less than 100 mW. The ability to rapidly prototype and optimize SJDs using additive manufacturing accelerates research and development in this field, paving the way for advanced thermal management solutions in real-world applications.

An integrated anthracnose management approach in Tommy Atkins mango cultivars in Cundinamarca - Colombia

Anthracnose, caused by Colletotrichum gloeosporioides in mango production, can lead to crop losses of 60 %. Synthetic fungicides constitute its leading management strategy. We evaluated combinations of different management practices to control anthracnose in a commercial Tommy Atkins mango grove in 2015 and 2016. We followed a randomized complete block experimental design with a subdivided plot arrangement composed of 12 treatments, three replicates per treatment, and one mango tree per replicate for 36 trees. Pruning was practiced at plot level (with and without pruning), nutrients were applied to subplots (soil fertilizer, foliar nutrient application, and no nutrient supply), and at sub-subplot level, three anthracnose management treatments were given (chemical, biological, and no treatment). In 2015 and 2016, the treatments involving natural or biological applications against anthracnose plus nutrient supply led to the most significant reductions in quiescent leaf infections and disease presence in flowers and fruits. In addition, pruning at specific crop development stages improved results. In light of our results, this integrated anthracnose management approach in mango production can deliver the expected results if implemented consistently.

A Matrix-Based Approach to Unified Synthesis of Planar Four-Bar Mechanisms for Motion Generation With Position, Velocity, and Acceleration Constraints

Abstract This paper introduces a novel matrix-based approach for the simultaneous type and dimensional synthesis of planar four-bar linkage mechanisms, accommodating various practical constraints, including position, velocity, acceleration, and joint placements. Traditional design processes segregate type synthesis, the determination of joint and link configurations, from dimensional synthesis, which involves specifying link sizes and pivot locations. This segregation often leads to complexities in addressing the complete design challenge. The novel methodology proposed in this paper departs from the conventional sequential design approach by concurrently evaluating type and dimensional parameters using a data-driven matrix formulation. The crux of the paper’s methodology involves formulating a singular design equation through a transformation matrix, parameterized by the Cartesian parameters of the mechanism’s dyads. This formulation linearly expresses a broad range of constraints, facilitating the identification of viable solutions through singular value decomposition and null space analysis. This integrated approach not only simplifies the synthesis process but also provides direct insights into the mechanism’s parameters, encompassing both type and dimensions, thereby obviating the need for further interpretative steps common to the use of quaternions and kinematic mapping. In essence, the paper presents two main contributions: the development of a unified design equation capable of encompassing a wide array of constraints within the mechanism synthesis process, and the introduction of an algorithm that effectively identifies all potential planar four-bar linkage mechanisms by accurately satisfying up to five constraints. This approach promises to enhance the design and optimization of mechanical systems by offering a more holistic and efficient pathway to mechanism synthesis.

Walrus Pipes and Waving Panpipes

Musical instruments play a fundamental role in speculative music creations. Standing at the intersection of various fields, they embody concepts, meanings, symbols and musical consonances that are specific to each and every culture. In the present age, mass-produced Western instruments are primarily made for musical geniuses in service of virtuosity, while they all sound the consonances of a single standardized tuning system. In contrast, the 3D printed wind instrument project presented here aims to be a counterpoint to these instruments. They are simple, custom-made new instruments that work together as an ensemble, in service of communal playing. For this reason, they can be used for diverse musical purposes: they are open, nonauthoritative, and as embodiments of new meanings and uncommon consonances, they might as well be agents and representatives of speculative music worlds. This article reveals the complete design process of two 3D printed wind instrument ensembles developed throughout the project: the Walrus Pipes and the Waving Panpipes.

Optimizing Well Integrity with Accurate Forecasting of Casing and Tubing Resistance Reduction Trends Through Innovative Numerical Modeling Techniques

Summary Well integrity is an essential aspect of well operations, and international standards are stringent on its application. Tubing and casing corrosion constitutes a major risk to well integrity and a challenge that must be analyzed preventively. Corrosion logs are programmed to verify the status of the barriers, but there is currently no methodology for planning future logs. In this paper, we propose a numerical modeling method to forecast tubular resistance reduction trends and estimate the barrier’s residual life. Corrosion logs are expensive and require the well to be shut in, which impacts the cost of the operation. Therefore, a numerical methodology for planning future logging is preferable to planning based on experience. Here we propose a methodology for predicting the point at which the downhole barriers will no longer satisfy the maximum allowable annular surface pressure (MAASP) criteria, using data from historical corrosion logs and a time-linear corrosion model. This innovative methodology recalculates MAASP for collapse and burst resistance of corroded pipes while considering well completion design and vertical depths resulting from the directional survey. The proposed methodology provides a comprehensive assessment of the corrosion progress, making it a more reliable tool for forecasting future corrosion trends. The methodology uses the last two corrosion logs for optimal prediction, but it also works when a single corrosion log is available. Numerical modeling is then used to calculate pipe resistance at each depth step of the LAS file, drawing a picture of the resistance vs. depth and time. The result of this new method is a chart that forecasts the progressive reduction of A-Annulus MAASP and the time at which it will cross the minimum criteria of the operator. Furthermore, the proposed methodology provides a probability window that takes into consideration the best- and worst-case scenarios. This is the first time that numerical modeling is used to recalculate MAASP and to forecast MAASP reduction trends in well barriers, based on corrosion/erosion measurements. The corrosion logging schedule can, hence, be programmed before MAASP reaches the operator’s limit, thus optimizing planning and costs for the operator. This, in turn, leads to a reduction in costs without affecting the overall operability and risking barrier failures.

Optimization of Composite Wheel Fender for Manned Lunar Vehicle

Abstract Weight reduction is the eternal theme of aerospace optimization. In the process of weight reduction, the main ways are material change, structural change, and processing method change. In the process of aerospace structure design, the boundary and load conditions are more complicated, which restricts the development of optimization technology in the aerospace field to some extent. At present, the research object of optimization technology in the aerospace field is mainly the large parts of the aerospace craft, and the structural optimization of small aerospace parts is rarely involved. Therefore, the research of structural optimization design method has important theoretical and engineering significance. A complete optimization design process is proposed, the structural performance of aviation products is simulated by finite element software, and the feasibility of the optimization theory is verified. This article mainly compares several schemes for weight reduction design of composite material wheel fenders in the process of design.

An Expectation-Maximization framework for Personalized Itinerary Recommendation with POI Categories and Must-see POIs

In this article, we introduce a novel deterministic method based on Expectation Maximization (EM) to solve the rather complex problem of designing a tourist trip or Personalized Itinerary Recommendation (PIR). PIR objective is to recommend a personalized tour consisting of successive Points of Interest (POIs), which maximizes user satisfaction and respects user time-frame constraints. On top of that, the POIs are divided into categories, in order for travelers to be able to set limits on the maximum (and minimum) number of POIs that belong to one category and are included in the itinerary. In the proposed framework, emphasis is given on the POIs sequence selection, which exploits the customized POI recommendations offered by a recommender system. Additionally, the proposed methodology with POIs categories is able to solve the TourMustSee problem, so that the tour includes a set of POIs that must be visited. The proposed system has been successfully incorporated into a mobile app, offering a complete tourist trip design. The high performance, resilience, and computational efficiency of the proposed framework are demonstrated by experimental findings and comparisons to existing approaches on numerous synthetic and real datasets.

Maize intercropped with Panicum maximum cultivars and Pigeon pea improves silage yield and quality

Integrated systems are efficient technologies used for diversifying production and promoting agricultural sustainability. The benefits of double intercropping are well-established in research. With advancements in sustainable food production technology, some studies have explored triple intercropping by adding legumes. However, triple intercropping methods must be comprehensively understood. This study aimed to evaluate the production of dry mass, fermentation profile, and nutritive values of silage from maize based intercropping. The experiment was conducted in the field, in Instituto Federal Goiano, Campus Rio Verde, State of Goiás, Brazil, where we assessed silage production and quality in monoculture and intercropping systems, in a randomized block complete design with three replications. We evaluated 8 treatments, consisting of silage from: maize monoculture; Tamani guinea grass monoculture; Quênia guinea grass monoculture; Zuri guinea grass monoculture; Pigeon pea monoculture; maize + Tamani guinea grass + Pigeon pea; maize + Quênia guinea grass + Pigeon pea and maize + Zuri guinea grass + Pigeon pea. The results showed that maize silage intercropped with cultivars of Panicum maximum (Tamani, Quênia and Zuri guinea grasses) and Pigeon pea increased silage mass production by 37.8% and the crude protein content at 25.1% compared with that of the monoculture maize silage. It also improved the fermentation characteristics of silage, reducing 5.1% of pH, 32.5% of buffering capacity, 28.9% of ammoniacal nitrogen in total nitrogen, 15.7% of effluent production, and 20.0% of dry matter losses, compared with those of grass and legume silage in monoculture. Thus, the silage produced by the triple intercropping proved to be an efficient technique for sustainable forage production.

Factorial design methodology for synthesizing zeolites: A critical review

AbstractThis review shows an outlook about several works which have used the factorial design methodology for synthesizing zeolites and mesoporous materials, included the use of wastes as sources of silicon and aluminum. In the bibliography several interesting works were identified with a complete development of the factorial design methodology. However, there are other works that showed a limited statistical analysis of the results. In spite of these issues, their contribution in the advance of synthesis of zeolites is important. Moreover, different ways to measure the crystallinity were detected being the X‐ray diffraction (XRD) the most used technique. This work also gives a proposal of the complete factorial design methodology as a guide in future works for synthesizing zeolites or zeo‐types materials. This work presents a detailed review of the methodology of designing experiments for the synthesis of zeolite or zeo‐type materials, where it has been shown and analyzed the type of design used, because the choice of a design involves a large number of experiments or few experiments thus facilitating the production of zeolite, in addition to the factors that influence the synthesis give information of the type to obtained zeolites, also includes a guide for applying this methodology. Experiment design methodology can help to produce high quality zeolites from virgin raw material or waste that impact the economy of the zeolite industry.

Combination of nitrogen and organic fertilizers reduce N2O emissions while increasing winter wheat grain yields and quality in China

Wheat grain yields, quality, and nitrous oxide (N2O) emissions were controlled through the type and application rate of nitrogen (N) fertilizer. Here, we investigated the optimal management of N fertilization by examining the combined effects of organic and N fertilizers on wheat yields, quality, and N2O emissions. Field trials under six treatments were located on campus farms at Anhui Science and Technology University, including farmer’s common practice (270 kg N ha-1, N270), 2/3 reduction in N270 (90 kg N ha-1, N90), organic fertilizer with equal N270 (OF270), 2/3 reduction in OF270 (OF90), 4/5 reduction OF270 + 1/5 reduction N270 (20% OF270 + 80% N270), and 4/5 reduction OF90 + 1/5 reduction N90 (20% OF90 + 80% N90) were applied to winter wheat. The plots were arranged in a randomized complete block experimental design. The N2O emissions were quantified under different fertilization measures in the peak wheat growing season during sowing, jointing, and grain filling stages, respectively. Compared with N270 and N90 treatments, N2O emissions were significantly decreased by 18.6% and 27.2%, respectively, under 20% OF270 + 80% N270% and 20% OF90 + 80% N90 (p < 0.05). Further, N2O emissions in N270 were increased by 50.8% relative to N90. Wheat yields increased significantly under 20% OF90 + 80% N90 by 27.6% (N270) and 16.4% (N90), and were considerably enhanced under 20% OF270 + 80% N270 by 40.6% (N270) and 12.7% (N90) in contrast to OF270 and N270 (p < 0.05). Compared with N90, the content of wet gluten, protein and starch under 20% OF90 + 80% N90 treatment significantly increased by 7.7%, 13.8% and 7.9%, and enhanced by 7.6%, 4.8%, 8.0% relative to OF90, respectively (p < 0.05). The starch content increased significantly by 2.0%, whereas the settlement value decreased considerably by 2.9% under 20% OF270 + 80% N270 (p < 0.05), and there was no notable difference in the wet gluten and protein contents (p > 0.05). Our findings indicated that organic fertilizer mixed with N fertilizer can effectively reduce N2O emissions, increase both the grain yields and quality in wheat field compared with N fertilizer alone.

Complete inverse design to customize two-dimensional dispersion relation via nonlocal phononic crystals

Complete inverse design to customize two-dimensional dispersion relation via nonlocal phononic crystals

Design and construction of a GAP-43 reporter system for potential identification of effective therapeutics for peripheral nerve regeneration

Background: Peripheral nerve injury (PNI) is a condition that can result in muscle paralysis and sensory disturbances. Electrical stimulation and/or the application of exogenous neurotrophic factors and cytokines are effective at enhancing nerve regeneration and is mediated via the expression of regeneration associated genes (RAGs) such as the growth associated protein GAP-43. Therapeutic upregulation of GAP-43 has potential use as a treatment for improving recovery from PNI. Few studies have investigated the potential of increasing GAP-43 for PNI therapeutic purposes and current methods for measuring GAP-43 expression are limited. Aims and Objectives: The broader aim of this work was to construct a motor neuron-like cell model with a GAP-43 reporter system. Such a model would have potential use in screening for novel therapeutics that upregulate GAP-43 and in the optimisation of electrical stimulation treatment in combination with these therapies. The key aim of the work was to design and construct a Cas9 expressing plasmid bearing a gRNA that targets GAP-43 cleavage and a donor plasmid bearing a reporter GFP or Neo cassette flanked with 5’ and 3’ GAP-43 homology arms (HAs) to facilitate the insertion of the reporter immediately 3’ of the GAP-43 promoter via CRISPR/Cas9 homology directed repair (HDR). Such an insertion would enable quantitative measurement of endogenous expression of the GAP-43 gene. Methods and Results: To guide the Cas9 nuclease to the target location, GAP-43 gRNA oligomers were designed and cloned downstream of the U6 promoter in the Cas9 expression plasmid px330, which also expresses the Cas9 gene and the cloned gRNA when transfected into cells. For CRISPR/Cas9 HDR, the 5’ and 3’ GAP-43 HAs were amplified from mouse genomic DNA and cloned into the donor plasmid using Gibson Assembly so that they flanked the reporter cassette. Results: This work successfully constructed the Cas9 gRNA expressing plasmid to target cleavage of the GAP-43 gene in mouse cell lines and has provided the complete design and construction foundation for generation of the reporter system for the endogenous GAP-43 gene in mice.

YIELD AND QUALITY RESPONSES OF SOYBEAN (Glycine max. L. Merr.) VARIETIES INOCULATED WITH RHIZOBIA STRAINS UNDER DROUGHT STRESS

The aim of this study was to investigate the effects of nodule formation and nodulation performance of Rhizobia bacteria on yield and quality in soybean roots under limited water application conditions in soybean varieties depending on climate change. In this study, 4 different irrigation applications (limited vs irrigated: 25%, 50%, 75%, 100%), 2 soybean varieties (Cinsoy and Altınay) and 3 different Rhizobia inoculants (Control, AZOTEK-2, USDA-110) were applied for two years (2020-2021) in order to determine the yield and quality characteristics of soybean. The experiment was established according to randomized complete block split-split plots experimental design with 3 replications. In the experiment, the main factor was Rhizobia inoculant treatments, the first sub-factor was soybean varieties, and the second sub-factor was irrigation applications. Within the scope of the study, yield and quality parameters such as plant height (cm), first pod height (cm), number of pods plant-1, number of seeds pod-1, seed yield (kg ha-1), 1000 seed weight (g), leaf area (cm2 plant-1), seed crude protein (%) and oil content (%) were examined. It was concluded that irrigation and inoculant applications and combinations of these factors had significant effects on yield parameters of soybean varieties. The study revealed that there was no discernible nodulation development observed in soybean roots under both irrigated and limited irrigation conditions. It was determined that under conditions of limited irrigation combined with high temperature conditions, the growth of the soybean was significantly impacted, resulting in a notable reduction in yield and leaf area but this was not observed in the quality characteristics.

Physics-Informed Deep-Learning Models Improve Forecast Scalability, Reliability

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4042557, “Physics-Informed Deep-Learning Models for Improving Shale and Tight Forecast Scalability and Reliability,” by Kainan Wang, SPE, Lichi Deng, and Yuzhe Cai, SPE, Chevron, et al. The paper has not been peer reviewed. _ In the complete paper, the authors present a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight reservoir forecasting. Their approach is applied to synthetic cases and many wells in the Permian Basin. Through hindsight studies, these models have been demonstrated to generate realistic and diverse production curves, capture the physics of unconventional flow, quantify well-production-outlook uncertainty, and help interpretation of subsurface uncertainty. Introduction In the realm of unconventional asset development, scalable forecasting is a key component in forecast reliability. In recent years, data-driven machine-learning models and workflows have emerged as potent tools for predicting well performance, particularly in scenarios where wells share similar reservoir properties, completion designs, and operational conditions. A novel approach known as physics-informed machine learning (PIML) has gained prominence. These models leverage the strengths of machine learning while honoring the constraints imposed by physical laws. By incorporating observational, inductive, or learning bias, they bridge the gap between empirical data and fundamental physics. In this paper, the authors showcase the application of a PIML system tailored specifically for unconventional reservoirs. Their methodology involves the selection of an appropriate physics modeling framework, coupled with the development of multiple machine-learning models that augment the limited field data set. Data Acquisition and Generation Bottomhole pressure (BHP) data are key to reliable forecasting because they reflect constraints to well production. Downhole pressure gauges can provide measurements of BHP accurately, but these measurements are not always available. Numerical models can be used to correlate surface measurements to BHP with some accuracy, taking into consideration the well configuration and fluid properties, yet it is too time-consuming to develop such models for many wells in relation to the speed needed for unconventional reservoir development. An in-house machine-learning model was developed for BHP calculation to further augment the data set of gauge measurements and numerical modeling. Through a workflow that chooses the wells with higher-fidelity data as a training set, and careful calibration and iterations between incrementally adding wells of high-fidelity data to reduce uncertainty, the trained machine- learning model is able to predict BHP accurately for many more wells. Another type of engineering data important for reliable well-performance analysis and forecasting is reservoir-fluid description. In this study, the authors leveraged an internally developed pressure/volume/temperature (PVT) machine-learning model trained on proprietary PVT data, which was also shown to provide reliable predictions to key PVT properties such as saturation pressure, formation volume factor, and viscosity from limited inputs at the reservoir and separator level. These PVT models not only enhance scalability of any downstream forecasting models but also play a crucial role in physics-informed uncertainty analysis for these models.