Low-cost Alternatives Research Articles

Analysis of Trait Variation and Correlations in Hybrid Rice Seed Production Using CMS Breeding Techniques

Imperfect panicle emergence in Cytoplasmic Male Sterile (CMS) lines is a hindrance for hybrid rice seed (F1) production. In CMS lines 10-15% of spikelets remaining enclosed within the flag leaf. This limits their availability for outcrossing, thereby reducing the efficiency of hybrid rice seed production. To mitigate this issue, the application of gibberellic acid (GA3) is a common practice. However, the high cost of GA3 poses a significant barrier for middle-class farmers involved in hybrid rice seed production. This study aims to identify low-cost chemical alternatives to GA3. We evaluated ten different treatments, including a control, on CMS (A) line rice plants. Our findings indicate that penicillin is the most effective treatment, offering high yield at a reduced cost. According to the yield component, treatment-2 (penicillin) reflected grain yield 71.26 g plant -1 which is highest among all the treatments. Potassium dichromate also performed well in promoting panicle emergence. Characters showing positive and significant correlations among different traits are expected to improve yield and can be selected for further advancement in hybrid rice seed production.

PERFORMANCE COMPARISON OF WINDING TYPES AND ROTOR STRUCTURE IN SYNCHRONOUS RELUCTANCE MOTOR

Synchronous Reluctance Motors (SynRMs) have attracted a lot of attention in recent years due to their simple construction, high efficiency and the fact that they do not require rare earth magnets. SynRMs produce torque by directing the magnetic flux through barriers in the rotor, making them a low-cost alternative. The performance of the motor can vary significantly depending on the design of the stator windings and rotor barriers. Different combinations of stator winding types and the number of rotor barriers directly affect the critical performance parameters of the motor such as torque, efficiency, torque ripple, output power, saliency ratio and copper consumption. In this study, SynRMs using six different stator winding types and two different rotor barrier structures (3 and 4 barriers) are analyzed. Output power, efficiency, torque, torque ripple, saliency ratio, d-q axis inductances depending on current phase angle, total losses and copper consumption are investigated. The effect of full pitch and shortened pitch configurations in winding structures on the performance is discussed comparatively. The effect of 3 and 4 barrier rotors on these parameters is also evaluated. These analyses aim to reveal the ways in which winding and rotor configurations in motor design can be used to optimize performance. SynRMs can provide energy efficiency and cost benefits in a variety of industrial applications, therefore selecting the proper configuration is of great importance from both an economic and performance perspective.

Predicting the replicability of social and behavioural science claims in COVID-19 preprints.

Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources. We elicited judgements from participants on 100 claims from preprints about an emerging area of research (COVID-19 pandemic) using an interactive structured elicitation protocol, and we conducted 29 new high-powered replications. After interacting with their peers, participant groups with lower task expertise ('beginners') updated their estimates and confidence in their judgements significantly more than groups with greater task expertise ('experienced'). For experienced individuals, the average accuracy was 0.57 (95% CI: [0.53, 0.61]) after interaction, and they correctly classified 61% of claims; beginners' average accuracy was 0.58 (95% CI: [0.54, 0.62]), correctly classifying 69% of claims. The difference in accuracy between groups was not statistically significant and their judgements on the full set of claims were correlated (r(98) = 0.48, P < 0.001). These results suggest that both beginners and more-experienced participants using a structured process have some ability to make better-than-chance predictions about the reliability of 'fast science' under conditions of high uncertainty. However, given the importance of such assessments for making evidence-based critical decisions in a crisis, more research is required to understand who the right experts in forecasting replicability are and how their judgements ought to be elicited.

ESTIMATION OF LAND COVER CHANGE USING LANDSAT SATELLITE IMAGERY AND THE RANDOM FOREST CLASSIFIER

The quantification of land cover change facilitates natural resource management. In general, these changes are determined at large scales but less frequently at a regional level. In this research, changes in land area covered by vegetation (V), agricultural use (A), grassland (G), and urban-rural (U) were estimated for the period 2002–2021 in the municipality of Huehuetla, in the Northern Sierra of Puebla, Mexico (39.5 km2), based on Landsat satellite images and the random forest classifier (RF). The latter was trained and evaluated with two datasets consisting of three spectral bands (red, green, and blue) and seven vegetation indices. RF performed well in classifying the four cover types at the beginning and end of the evaluation period. RF obtained an overall correct classification accuracy of 92.5 % in 2002 and 92.3 % in 2021. At the land cover level, RF identified vegetation cover with an F1 score of 100 % in 2002 and 98.2 % in 2021; however, it identified the urban-rural cover less effectively, with an F1 of 71.5 % in 2002 and 81.8 % in 2021. In the period analyzed, the urban area increased from 1.7 to 6.4 % (an increase of 4.7 % of the total area), and the vegetation area from 48.1 to 68.7 % (an increase of 15.6 %), at the expense of a reduction in the grassland area (19.8 %), while the agricultural area remained stable (reduction of 0.5 %). This study illustrates the importance of using machine learning techniques and satellite images to assess land cover changes at the regional level as a viable and low-cost alternative.

Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy

Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost alternative that increases datapoints and map accuracy. This study tests and optimizes a methodology for high-detail soil mapping in a 2.5 ha hazelnut grove in Grosseto, Southern Tuscany, Italy, using both EMI sensors (GF Mini Explorer, Brno, Czech Republic) and a handheld NIR spectrometer (Neospectra Scanner, Si-Ware Systems, Menlo Park, CA, USA). In addition to two profiles selected by clustering, another 35 topsoil augerings (0–30 cm) were added. Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). Partial least square regression (PLSR) with a national spectral library, augmented by the five local samples, predicted clay, sand, organic carbon (SOC), total nitrogen (TN), and cation exchange capacity (CEC). The 37 predicted datapoints were used for spatial interpolation, using the ECa map, elevation, and DEM derivatives as covariates. Kriging with external drift (KED) was used to spatialize the results. The errors of the predictive maps were calculated using five additional validation points analyzed by conventional methods. The validation showed good accuracy of the predictive maps, particularly for SOC and TN.

Boosting 1H and 13C NMR signals by orders of magnitude on a bench.

Sensitivity is often the Achilles' heel of liquid-state nuclear magnetic resonance (NMR) experiments. This problem is perhaps most pressing at the lowest fields (e.g., 80-MHz 1H frequency), with rapidly increasing access to NMR through benchtop systems, but also sometimes for higher-field NMR systems from 300 MHz to 1.2 GHz. Hyperpolarization by dissolution dynamic nuclear polarization (dDNP) can address this sensitivity limitation. However, dDNP implies massive and complex cryogenic and high-field instrumentation, which cannot be installed on the bench. We introduce here a compact helium-free 1-T tabletop polarizer as a simple and low-cost alternative. After freezing and polarizing the frozen analyte solutions at 77 K, we demonstrate 1H signal enhancement factors of 100, with rapid 1-s buildup times. The high polarization is subsequently transferred by 1H→13C cross polarization (CP) to 13C spins. Such a simple benchtop polarizer, in combination with hyperpolarizing solid matrices (HYPSOs), may open the way to replenishable hyperpolarization throughout multiple liquid-state NMR experiments.

Effect of Laser Pre-Treatment on Peanut Seeds on Resveratrol Con-tent and Fatty Acid Profile in Field-Harvested Seeds

Objective: To evaluate the effects of laser radiation on peanut seeds as a pretreatment on the resveratrol content and fatty acid profile in the harvested seeds. Design/methodology/approach: The experiment was established under field conditions located in the municipality of Nicolás Bravo, Chiapa de Corzo, Chiapas. The seeds were irradiated with a red laser with a wavelength of 636 nm and an intensity of 120 mW. The exposure time of the seeds to the laser was 15 min and non-irradiated seeds were used as a control. Results: Laser radiation on seeds increased the resveratrol content in the harvested peanut seeds compared to control seeds, and changes were also observed in some fatty acids in peanut oil such as butyric acid, stearic acid and cis 11,14,17 eicosatrienoic acid. Findings/conclusions: laser irradiation applied to seeds is a low-cost biotechnological alternative that allows generating positive changes in the quality of grains from seeds harvested under field conditions.

Optimizing the Dermatologic Formulary at a Free Clinic.

Free clinics provide care to underserved populations nationwide. The Birmingham Free Clinic offers dermatology care and operates an onsite dispensary. Little is known about specialty care services and medication optimization in the free clinic setting. This study analyzed dermatologic diagnoses and medication distribution patterns to optimize the dermatologic formulary at a free clinic. A retrospective chart review was performed of dermatology clinic visits between January 2020 and May 2022. The data analysis used descriptive statistics. Fifty-seven patients received 158 dermatologic diagnoses and were prescribed 216 medications. The five most common diagnoses were dermatitis (24.1%, 38/158), psoriasis (13.9%, 22/158), acne/rosacea (10.1%, 16/158), alopecia (8.9%, 14/158), and superficial bacterial/fungal infections (6.3%, 10/158). The five most frequently prescribed medications were triamcinolone 0.1% cream (13.4%, 29/216), doxycycline 100 mg (10.6%, 23/216), triamcinolone 0.1% ointment (5.6%, 12/216), ketoconazole 2% shampoo (4.6%, 10/216), and hydrocortisone 2.5% cream (3.7%, 8/216). Fifty-two percent of all prescribed medications were provided onsite. Triamcinolone 0.1% ointment and ketoconazole 2% shampoo were usually unavailable onsite. Eighty-gram tubes of triamcinolone 0.1% ointment were added to the dermatologic formulary, and low-cost alternatives were suggested for ketoconazole 2% shampoo. This study provides a model for formulary optimization in free clinics with specialty care services.

Green synthesis of biodiesel from magnetic basic biochar derived from Amazonian murici residual biomass: Optimization, kinetic, thermodynamic, and environmental studies

There is a growing need for sustainable catalysts in biodiesel production, however, efficient and low-cost alternatives derived from agro-industrial residues remain limited. This study addresses this gap by exploring murici biochar as a potential source for catalytic development. A basic magnetic catalyst, based on biochar from an Amazonian agro-industrial residue and consisting of sodium impregnated in murici biochar with cobalt ferrite as the magnetic phase, was produced for application in the transesterification reaction of soybean oil. The catalyst was synthesized by wet impregnation with different sodium concentrations to determine the optimal concentration of active phase in the synthesis process. The catalyst demonstrated the best performance and was characterized by TGA, XRD, FT-IR, SEM, EDS, and VSM. A central face-centered composite design was used in the development of a mathematical model for the prediction of ester content. The biodiesel obtained in the optimized reaction conditions of temperature of 90 °C, reaction time of 1.4h, methanol:oil (MeOH:oil) molar ratio of 17:1, and catalyst concentration of 7% obtained ester content of 97.11% and presented physicochemical properties according to the limits established in the ASTM D6751. Moreover, the kinetic and thermodynamic studies revealed that the process follows the pseudo-first order. It also presents an endothermic and not spontaneous character. The green reaction parameters indicate that the reaction using the catalyst is sustainable with low waste generation. Thus, this study show the feasibility of applying the agro-industrial residue in the synthesis of environmentally friendly and low-cost magnetic basic catalysts for the production of biodiesel.

Acute Kidney Injury after Hypoxic Ischemic Encephalopathy in Neonates Treated with Passive Versus Active Total Body Cooling.

Hypoxic-ischemic encephalopathy (HIE) affects 1-2 per 1000 term live births, often resulting in severe long-term disabilities. Therapeutic hypothermia (TH) is the standard care in developed countries, but high costs of modern cooling devices necessitate low-cost alternatives. This study compares passive cooling with active machine cooling regarding short-term renal outcomes, specifically acute kidney injury (AKI), in neonates with HIE. This retrospective study was conducted at Dr. Soliman Fakeeh Hospital's neonatal intensive care unit from 2019 to 2023. The study analyzed patient demographics, clinical outcomes, and laboratory data (sodium, potassium, urea, and creatinine) to assess AKI. Treatment involved whole-body cooling at 33.5-34.5°C for 72 hours, followed by gradual rewarming. A total of 39 neonates were included in the study. Both cooling methods showed similar short-term renal outcomes, with no statistically significant differences in creatinine levels between the groups at baseline, 24 hours, 72 hours, or discharge. A trend of higher creatinine levels in the passive cooling group was observed, but it did not reach statistical significance. The median length of hospital stay was longer in the passive cooling group, though this difference was marginally nonsignificant. Long-term follow-up revealed no significant differences in chronic kidney disease incidence or neurodevelopmental outcomes between the groups. This study found no significant differences in both short-term renal outcomes and long-term effects between passive and active cooling methods in neonates with HIE. However, the trend of higher creatinine levels in the passive cooling group suggests the need for further investigation with larger sample sizes and extended follow-up to clarify the long-term effects of cooling methods on renal and neurodevelopmental outcomes in neonates with HIE.

Effect of Treatment Methods on Material Properties and Performance of Sawdust-Concrete and Sawdust-Polymer Composites.

The circular economic approach in polymer composite research has gained acceptance for offering low-cost, high-performance solutions. Sawdust-derived composites have drawn interest as alternatives in concrete and composite fabrication, addressing housing shortages and resource depletion. Sawdust concrete (SDC) and sawdust polymer composites (SDPC) are key areas under investigation, with SDC additionally aiding in carbon reduction in building materials. However, challenges arise due to sawdust's inherent hydrophilicity, porosity, and lower strength. This study introduces a novel approach by identifying specific chemical treatments, including alkali and silane, which effectively enhance sawdust's compressive and tensile strengths, moisture resistance, and durability, optimizing it for structural applications. The study evaluates SDC's compressive strength based on treatment type, concentration, and curing time, examining physical properties such as water absorption, moisture sensitivity, and fiber-matrix adhesion. The unique contribution lies in a detailed optimization analysis, revealing conditions under which sawdust reaches structural-grade performance, expanding its potential in sustainable construction. For SPDC, tensile strength improvements are assessed under various chemical compositions, showing that specific polymers form stronger fiber-matrix bonds for greater stability. Morphological studies further explore fiber-matrix compatibility, hydrophobicity, and failure mechanisms. By advancing the understanding of treatment efficacy, this review positions sawdust as a viable, low-cost material alternative, establishing a foundation for sustainable innovation in construction and bio-composite research. These findings contribute to sawdust's potential as a practical, eco-friendly building material.

Ternary Ionic Liquid Analogues for Ambient and Low-Temperature Rechargeable Aluminum Batteries

Many issues surround lithium, such as scarcity, safety, and ethical extraction. One promising alternative to lithium-ion batteries are aluminum batteries, which offer the earth abundance, inherent safety, high theoretical capacity, and low cost of aluminum metal. Additionally, batteries for transportation, space, or defense applications may often need to operate at low temperatures, where electrolytes exhibit lower ionic conductivity and are prone to freezing. The state-of-the-art electrolytes for aluminum batteries, Lewis acidic AlCl3 (aluminum chloride)-[EMIm]Cl (1-ethyl-3-methyl-imidazolium chloride) ionic liquids (ILs), are not cost effective and exhibit low ionic mobilities and conductivities below 0 oC. Their electrochemical and thermophysical properties can be tuned by adding a third species, such as urea. The AlCl3-urea ionic liquid analogue has also been investigated as a lower cost alternative. However, little research has been done on ternary mixtures of AlCl3-urea-[EMIm]Cl, including investigations of their physical properties and ability to reversibly electrodeposit Al metal from ambient to low temperatures.In this work, AlCl3-urea-[EMIm]Cl mixtures with molar ratios of 1.3:X:(1-X) molar ratios were synthesized, where X = 0, 0.125, 0.25, 0.5, 0.75, and 1. Physical and electrochemical properties were characterized from ambient to low temperatures (down to -70 oC) with spectroscopic, thermoanalytical, and electrochemical measurements. Differential scanning calorimetry (DSC) was utilized to observe the thermal behavior of the electrolytes and determine their liquid-phase temperature windows. Quantitative liquid-state 27Al and 1H single-pulse nuclear magnetic resonance (NMR) experiments revealed how the types and populations of the polyatomic aluminum complexes changed based on urea concentration, as well as understand how the local environments of the EMIm+ cations and urea change across different compositions. The Al electrodeposition capability of the electrolyte mixtures were tested from 25 to -70 oC, both galvanostatically (two-electrode cells) and potentiodynamically (three-electrode cells). Subsequently, the most promising electrolyte compositions were tested in Al-graphite batteries to determine their technological feasibility and electrochemical properties, particularly at lower temperatures. The results indicate that AlCl3-urea-[EMIm]Cl in a 1.3:0.25:0.75 molar ratio can be used to enhance the electrochemical performance, reduce the cost, and expand the operating temperature window of rechargeable aluminum batteries compared to the AlCl3-[EMIm]Cl standard.

In Operando Raman Spectroscopy – a Powerful Tool for Understanding the Chemistry and Electrochemistry of Aqueous Redox Flow Batteries

Of the many emerging technologies for low-cost, grid-scale energy storage, redox flow batteries have been an area of active investigation for decades. More recently, a focus on lower cost alternatives to the foundational Vanadium-based redox flow batteries has been a major focus of the flow battery research community. Specifically, there is great interest in redox active species based on Earth-abundant materials suitable for aqueous systems such that the overall cost per kWh of energy storage can be minimized to enable economical and reliable grid storage to utilize energy generated from intermittent, renewable sources.The use of aqueous electrolytes, ideally at mild pH, in conjunction with inexpensive and highly soluble active material presents an exciting challenge for grid-scale flow batteries. Many new aqueous-soluble, redox active compounds have garnered interest, including water-soluble organic redox species and low-cost transition-metal complexes based on Fe or other earth-abundant metals. Recently, our group has devoted significant attention to new anolytes based on Fe(II)/Fe(III) complexes with various types of low-cost and easily synthesizable phosphonate ligands. Specifically, the nature and fine-tuning of phosphonate ligands allows a high degree of control over the redox potential, rate capability and kinetics, and stability of Fe-based complexes for flow battery anolytes. The wide variety of potential ligands with different functional moieties sometimes produces complicated electrochemical behavior, which has motivated us to apply in operando analysis to inform interpretation of electrochemical and battery cycling results.This presentation focuses on one such analytical tool, namely Raman spectroscopy, applied in operando to several flow batteries based on various Fe-phosphonate anolyte complexes to understand the effects of various substituents and functional groups on the Fe(II)/Fe(III) redox behavior, potential, kinetics, and even structural transformations of these complexes during battery cycling. Examples based on recently reported nitrilotri(methylphosphonic acid) (NTMPA) and related phosphonic acid ligands and their resultant Fe-complex-based anolytes will be presented showing that online vibrational spectroscopy can provide clarity on the effects of varied functional groups on both the molecular structure of redox-active Fe-complexes and the effects of ligand binding on kinetics. This experimental tool beneficially compliments and confirms results from density functional theory to elucidate the structure of these Fe-complexes. Further, the multivariate spectral response provided by in operando Raman spectroscopy allows the state of charge of the cell to be monitored readily. Overall, we aim to highlight the utility of Raman spectroscopy in probing the chemical transformations that occur during electrochemical measurements and battery testing in real time, with potential benefits to a much wider variety of promising redox flow battery systems and the flow battery community at large. Figure 1

Photocatalytic ammonia synthesis from nitrogen in water using iron oxides: Comparative efficiency of goethite, magnetite, and hematite

Photocatalytic ammonia synthesis from nitrogen and water presents a promising pathway for decentralized sustainable ammonia production, leveraging the abundant solar energy. In this study, we explore the efficacy of three iron oxide polymorphs – goethite (α-FeO(OH)), magnetite (Fe3O4), and hematite (α-Fe2O3) – as photocatalysts for nitrogen reduction under ultraviolet (UV) light. The materials were synthesized using hydrothermal and polymeric precursor methods, characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), UV–Vis spectroscopy, photoluminescence spectroscopy, and thermal analysis to understand their structural, surface, and optoelectronic properties. Among the materials tested, goethite demonstrated the highest ammonia production rate (20.6 µmol g−1h−1), which we attribute to its larger specific surface area and the stability of its surface hydroxyl groups, which play a critical role in facilitating the protonation and electron transfer necessary for nitrogen reduction. Curiously, magnetite also displayed some activity (10.3 µmol g−1h−1), likely due to the formation of a heterojunction with the co-occurring goethite phase. Hematite showed the fastest area-based production rate (1.05 µmol m−2h−1), suggesting it is the polymorph with highest density of active sites for N2 reduction. This work contributes to the ongoing search for greener and lower-cost alternatives to the Haber-Bosch process, with implications for both agriculture and energy storage.

Stoichiometric Regeneration of Biomimetic Nicotinamide Coenzyme Powered by Biomass Sugars via In Vitro Synthetic Enzymatic Biosystems.

Biomimetic nicotinamide coenzymes, including nicotinamide mononucleotide (NMN+), have been demonstrated as promising low-cost alternatives to nicotinamide adenine dinucleotide (phosphate) (NAD(P)+) in biocatalysis. Herein, to efficiently regenerate NMNH from NMN+ in vitro powered by biomass sugars, a thermophilic NADP+-dependent glucose 6-phosphate dehydrogenase from Thermotoga maritima (TmG6PDH) was engineered to increase the activity toward NMN+. The catalytic efficiency (kcat/Km) of optimal mutant (TmG6PDH-R7) toward NMN+ increased by 71.7-fold than TmG6PDH-WT. As a result, compared to the wild type, the coenzyme specificity ([kcat/Km]NMN +/[kcat/Km]NADP +) of TmG6PDH-R7 increased by ~2.0×105-fold. The structural analysis revealed that the introduced hydrophobic and bulky residues lead to the formation of a smaller binding pocket, which resulting in a higher affinity for NMN+ with small size than NADP+. Then several in vitro synthetic enzymatic biosystems (ivSEBs) comprising this thermophilic TmG6PDH-R7 and a previously engineered thermophilic 6-phosphogluconate dehydrogenase were constructed. These ivSEBs harnessed the complete oxidation of renewable biomass sugars to facilitate the stoichiometric regeneration of 12 molecules of NMNH from 1 molecule of glucose, thereafter producing various products such as levodione, 2,3-butanediol or bioelectricity, over a wide temperature range. This study could pave the way for using stable and low-cost biomimetic coenzymes in ivSEBs for industrial biomanufacturing.

Illumina SBS Sequencing and DNBSEQ Perform Similarly for Single-Cell Transcriptomics.

High-throughput single-cell RNA sequencing (scRNA-seq) workflows produce libraries that demand extensive sequencing. However, standard next-generation sequencing (NGS) methods remain expensive, contributing to the high running costs of single-cell experiments and often negatively affecting the sample numbers and statistical strength of such projects. In recent years, a plethora of new sequencing technologies have become available to researchers through several manufacturers, often providing lower-cost alternatives to standard NGS. In this study, we compared data generated from mouse scRNA-seq libraries sequenced with both standard Illumina sequencing by synthesis (Illumina SBS) and MGI's DNA nanoball sequencing (DNBSEQ). Our findings reveal similar overall performance using both technologies. DNBSEQ exhibited mildly superior sequence quality compared to Illumina SBS, as evidenced by higher Phred scores, lower read duplication rates and a greater number of genes mapping to the reference genome. Yet these improvements did not translate into meaningful differences in single-cell analysis parameters in our experiments, including detection of additional genes within cells, gene expression saturation levels and numbers of identified cells, with both technologies demonstrating equally robust performance in these aspects. The data produced by both sequencing platforms also produced comparable analytical outcomes for single-cell analysis. No significant difference in the annotation of cells into different cell types was observed and the same top genes were differentially expressed between populations and experimental conditions. Overall, our data demonstrate that alternative technologies can be applied to sequence scRNA-seq libraries, generating virtually indistinguishable results compared to standard methods, and providing cost-effective alternatives.

Techno-Economic Analysis of Near-Infrared (NIR) Systems at Feed Millsas a Low-Cost, High-Speed Alternative to Feed Ingredient Testing

Improperly balanced diets not only impact the quality of animal production but also the profits of a livestock operation. Typically, the nutrient and chemical content of feed ingredients and forages are determined using well-established wet chemistry tests. However, these tests can be expensive and time-consuming. Moreover, the increasing use of various by-products which are known to have large variations in chemical and nutrient content warrants a real-time on-site feed and forage testing system. Near infrared (NIR) spectroscopy systems are quick and effective on-site testing tools. While NIR systems are being adopted for on-farm or at feed mill ingredient and forage testing, little is known about the economic impacts of such an investment for a livestock or feed mill operator. This study developed a baseline model and an Excel based spreadsheet application for performing Return on Investment (ROI) analysis to determine the feasibility of using an on-farm or at feed mill NIR testing system. ROI was calculated based on the nutrient cost saved or spent determined from the difference of estimated nutrient content and actual calculated value. The findings from this study will promote low-cost alternatives for onfarm or at feed mill ingredient testing, positively impacting quality of animal products and minimizing costs.

Developing a self-calibrating system for volume measurement of spheroidal particles using two acoustically levitated droplets.

Acoustically levitated droplets in the nanoliter to microliter range are studied in various fields. The volume measurements of these are conventionally done using image analysis. A precision-produced calibration sphere is often used to calibrate the recording equipment, which is time-consuming and expensive. This paper describes a self-calibrating method to measure the volumes of acoustically levitated droplets as a versatile and low-cost alternative. The distance between two levitated droplets in a horizontally oriented acoustic trap is processed via real-time or recorded frame data using image analysis. To assist in setting the cavity length for the acoustic trap, a simulation of the acoustic field is utilized based on the temperature in the trap, thereby also predicting the distance between the central nodes used to determine the scale factor. The volumes of the spheroidal-shaped levitated droplets can then be calculated from the pixel data. We use a modified version of the well-known TinyLev, and our method has been tested with two types of transducer packing. Its accuracy for volume measurements has been verified in comparison with the standard calibration sphere technique. Self-calibration of the system is demonstrated by changing the camera zoom during data collection, with negligible effects on measured volume. This is something that could not be achieved with conventional static methods.

Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms

Manual selection of fresh fruit has been identified as a significant challenge for the agricultural sector due to its time-consuming nature and potential for inconsistent categorization. This process requires human labour to visually inspect and sort fruits, leading to variability and inefficiencies in the sorting process. This research proposes a low-cost alternative using intelligent fruit selection systems based on computer vision techniques to address these issues. These systems aim to automate the process of fruit selection, improving efficiency and consistency in categorizing fruits such as apples, bananas, and oranges. A critical step in developing such intelligent systems is the feature extraction process. Feature extraction is essential in classification, especially for data sources in the form of images. It involves identifying and isolating relevant information from the images that classification algorithms can use to distinguish between different fruit categories. If the feature extraction process fails to capture the correct information, the performance or accuracy of the classification algorithm will be negatively impacted. This research compares three different methods for extracting features from fruit images to determine which method yields the highest accuracy for fruit classification. The feature extraction methods evaluated were Grayscale Pixel Values, Mean Pixel Value of Channels, and Extracting Edge Features. The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. CNNs are well-suited for image classification tasks due to their ability to learn hierarchical feature representations from the input images automatically. By comparing the performance of the CNN classifier using the three different feature extraction methods, this research aims to identify the method that provides the highest level of accuracy in classifying fruit images. By conducting this comparative analysis, the research provides insights into the most effective feature extraction techniques for improving the performance of computer vision-based fruit selection systems, ultimately contributing to more efficient and consistent fruit categorization in the agricultural sector. The result revealed that the Grayscale achieved the highest validation accuracy (99.94%) and the lowest validation loss (0.44%).

Advancing solar thermal energy systems: Comparative study of low-cost receivers in parabolic trough collectors

The growing demand for efficient and cost-effective solar thermal energy solutions underscores the importance of innovative receiver designs in parabolic trough collector (PTC) systems. Despite the reliability of conventional designs, there is a need for low-cost alternatives that are accessible for widespread use, particularly in developing regions. However, a notable absence of comparative studies using the International Organization for Standardization (ISO) norm, the ISO 9806:2017 standard, which is the most current framework for thermal performance testing, thus highlighting a critical area for exploration. This study aims to develop and evaluate two low-cost receiver designs for a PTC system equipped with a stainless-steel reflector: one featuring a U-pipe within an evacuated glass tube, and the other using inlet and outlet pipes. Experimental testing was conducted according to the ISO 9806:2017 standard to assess their impact on the thermal efficiency of the collecting system. Additionally, we validated our results against previous research findings, which employed the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) norm, the ASHRAE 93 standard, which is the older and more rigorous. The thermal efficiencies were determined to be 43.79% for the U-pipe receiver and 49.77% for the inlet/outlet pipes receiver. The latter design shows a reduced overall heat loss coefficient, caused by the increased heat transfer surface area and the direct contact between the water and the absorber. These findings highlight the potential of the inlet/outlet pipes receiver as a cost-effective and efficient solution, particularly in developing regions.