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Article ; Online: Smart-sight: Video-based waste characterization for RDF-3 production.

Tahir, Junaid / Tian, Zhigang / Martinez, Pablo / Ahmad, Rafiq

2024 Volume 178, Page(s) 144–154

Abstract: A material recovery facility (MRF) can transform municipal solid waste (MSW) into a valued commodity called refuse-derived fuel (RDF) as a promising solution to waste-to-energy conversion. The quality of the produced RDF significantly relies on the ... ...

Abstract	A material recovery facility (MRF) can transform municipal solid waste (MSW) into a valued commodity called refuse-derived fuel (RDF) as a promising solution to waste-to-energy conversion. The quality of the produced RDF significantly relies on the composition of in-feed waste and waste characterization method applied for auditing purposes, a process that is both time-consuming and fraught with potential hazards. This study focuses to enhance the workflow of the waste characterization process at an MRF. A solution named Smart Sight is proposed to detect and classify waste based on videos recorded after processing MSW through a mechanical sorting line consisting of bag breakers and trommel screens. A comprehensive dataset is created encompassing thirteen mixed waste classes from single and multi-family streams. The dataset is preprocessed with motion compensation techniques and frame differencing methods to extract and refine valuable frames. A one-stage YOLO detector model is then trained over the dataset. The experimental results show that the proposed method works efficiently at detecting and classifying waste objects in indoor MRF environments. Accuracy, precision, recall, and F1 score related to the proposed solution are found to be 0.70, 0.762, 0.69 and 0.72, respectively, with a mAP@
MeSH term(s)	Refuse Disposal/methods ; Garbage ; Solid Waste/analysis
Chemical Substances	Solid Waste
Language	English
Publishing date	2024-02-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	2001471-5
ISSN	1879-2456 ; 0956-053X
ISSN (online)	1879-2456
ISSN	0956-053X
DOI	10.1016/j.wasman.2024.02.028
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Design of Experiments to Compare the Mechanical Properties of Polylactic Acid Using Material Extrusion Three-Dimensional-Printing Thermal Parameters Based on a Cyber-Physical Production System.

Castillo, Miguel / Monroy, Roberto / Ahmad, Rafiq

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 24

Abstract: The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms ... ...

Abstract	The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms of its mechanical properties remains important to extend the lifespan of numerous critical applications. To enhance the monitoring of mechanical properties during printing, it is necessary to understand the relationship between temperature profiles and ultimate tensile strength (UTS). This study uses a cyber-physical production system (CPPS) to analyze the impact of four key thermal parameters on the tensile properties of polylactic acid (PLA). Layer thickness, printing speed, and extrusion temperature are the most influential factors, while bed temperature has less impact. The Taguchi L-9 array and the full factorial design of experiments were implemented along with the deposited line's local fused temperature profile analysis. Furthermore, correlations between temperature profiles with the bonding strength during layer adhesion and part solidification can be stated. The results showed that layer thickness is the most important factor, followed by printing speed and extrusion temperature, with very close influence between each other. The lowest impact is attributed to bed temperature. In the experiments, the UTS values varied from 46.38 MPa to 56.19 MPa. This represents an increase in the UTS of around 17% from the same material and printing design conditions but different temperature profiles. Additionally, it was possible to observe that the influence of the parameter variations was not linear in terms of the UTS value or temperature profiles. For example, the increase in the UTS at the 0.6 mm layer thickness was around four times greater than the increase at 0.4 mm. Finally, even when it was found that an increase in the layer temperature led to an increase in the value of the UTS, for some of the parameters, it could be observed that it was not the main factor that caused the UTS to increase. From the monitoring conditions analyzed, it was concluded that the material requires an optimal thermal transition between deposition, adhesion, and layer solidification in order to result in part components with good mechanical properties. A tracking or monitoring system, such as the one designed, can serve as a potential tool for reducing the anisotropy in part production in 3D printing systems.
Language	English
Publishing date	2023-12-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23249833
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems

Abbasi, Rabiya / Martínez, Pablo / Ahmad, Rafiq

Agriculture. 2023 Mar. 03, v. 13, no. 3

2023

Abstract: Chlorosis, or leaf yellowing, in crops is one of the quality issues that primarily occurs due to interference in the production of chlorophyll contents. The primary contributors to inadequate chlorophyll levels are abiotic stresses, such as inadequate ... ...

Abstract	Chlorosis, or leaf yellowing, in crops is one of the quality issues that primarily occurs due to interference in the production of chlorophyll contents. The primary contributors to inadequate chlorophyll levels are abiotic stresses, such as inadequate environmental conditions (temperature, illumination, humidity, etc.), improper nutrient supply, and poor water quality. Various techniques have been developed over the years to identify leaf chlorosis and assess the quality of crops, including visual inspection, chemical analyses, and hyperspectral imaging. However, these techniques are expensive, time-consuming, or require special skills and precise equipment. Recently, computer vision techniques have been implemented in the agriculture field to determine the quality of crops. Computer vision models are accurate, fast, and non-destructive, but they require a lot of data to achieve high performance. In this study, an image processing-based solution is proposed to solve these problems and provide an easier, cheaper, and faster approach for identifying the chlorosis in lettuce crops grown in an aquaponics facility based on their sensory property, foliage color. The ‘HSV space segmentation’ technique is used to segment the lettuce crop images and extract red (R), green (G), and blue (B) channel values. The mean values of the RGB channels are computed, and a color distance model is used to determine the distance between the computed values and threshold values. A binary indicator is defined, which serves as the crop quality indicator associated with foliage color. The model’s performance is evaluated, achieving an accuracy of 95%. The final model is integrated with the ontology model through a cloud-based application that contains knowledge related to abiotic stresses and causes responsible for lettuce foliage chlorosis. This knowledge can be automatically extracted and used to take precautionary measures in a timely manner. The proposed application finds its significance as a decision support system that can automate crop quality monitoring in an aquaponics farm and assist agricultural practitioners in decision-making processes regarding crop stress management.
Keywords	agriculture ; aquaponics ; automation ; chlorophyll ; chlorosis ; color ; computer vision ; crop quality ; decision making ; decision support systems ; equipment ; farms ; humidity ; leaves ; lettuce ; lighting ; models ; sensory properties ; stress management ; temperature ; water quality
Language	English
Dates of publication	2023-0303
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2651678-0
ISSN	2077-0472
ISSN	2077-0472
DOI	10.3390/agriculture13030615
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Calorific value prediction models of processed refuse derived fuel 3 using ultimate analysis

Tahir, Junaid / Ahmad, Rafiq / Tian, Zighang

Biofuels. 2023 Jan. 2, v. 14, no. 1 p.69-78

2023

Abstract: Various models have been developed to predict the calorific value of Biomass but only a few models exist to predict this measure for the urban waste like Refuse Derived Fuel (RDF). In this paper, new models are introduced to predict the calorific value ... ...

Abstract	Various models have been developed to predict the calorific value of Biomass but only a few models exist to predict this measure for the urban waste like Refuse Derived Fuel (RDF). In this paper, new models are introduced to predict the calorific value of RDF, as more advanced studies are required to be conducted with a focus on a distinct group of RDFs for validating the robustness of the models in the existing literature. The calorific value based on ultimate (elemental) analysis considers the contents of C, H, N, S, and O elements in RDF. Using empirical and machine learning methods, the newly established models accurately predicted the calorific value of the samples provided by a local municipality situated in Edmonton, Alberta, Canada. Furthermore, these new models demonstrated a lower bias and average absolute error than the other twelve previously published models pertinent to RDF material. Based on the established workflow the ultimate analysis-based models gave a higher coefficient of determination (R²) value in the range 0.78 − 0.80, indicating that the developed model improves the prediction of calorific value for RDF. The newly developed machine-learning models showed better results than the empirical models developed in this study implying that complex correlations can be dealt effectively while predicting calorific values for RDF.
Keywords	biofuels ; biomass ; models ; prediction ; solid wastes ; Alberta ; Higher heating value ; ultimate analysis ; refused derive fuel ; American Society for Testing and Materials
Language	English
Dates of publication	2023-0102
Size	p. 69-78.
Publishing place	Taylor & Francis
Document type	Article ; Online
ISSN	1759-7277
DOI	10.1080/17597269.2022.2116771
Database	NAL-Catalogue (AGRICOLA)

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Article: An open-source powered and ergonomic personal protective respirator for frontline COVID-19 response.

Villanueva, Emanuel Martinez / Ahmad, Rafiq

HardwareX

2021 Volume 10, Page(s) e00223

Abstract: Intending to shield front-liners who are currently exposed to COVID-19, and because of the lack of proper powered air-purifying respirator, this study shows the design and development of an open-source ergonomic respirator with a washable filter. This ... ...

Abstract	Intending to shield front-liners who are currently exposed to COVID-19, and because of the lack of proper powered air-purifying respirator, this study shows the design and development of an open-source ergonomic respirator with a washable filter. This device has an estimated working time of 12 h, and the tests' airflow always showed a value over 4.5 cubic feet per minute, a higher value than the national institute for occupational safety and health specification for full-face closed respirators. The proposal relies on 3D printing technology for all the custom-design parts and usages easy-to-access components for the rest of the material. The mask for the APRPAPR in the article has a defogging feature, 180 degrees of viewing angle, an ergonomic profile, and no obstruction on the mouth to show the user's full face. This respirator has an estimated cost of 318 USD, approximately one-third of the market's price of well-known brands.
Language	English
Publishing date	2021-08-13
Publishing country	England
Document type	Journal Article
ISSN	2468-0672
ISSN	2468-0672
DOI	10.1016/j.ohx.2021.e00223
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Article: Low apoplastic Na

Mahmood, Moniba Zahid / Odeibat, Hamza Ahmad / Ahmad, Rafiq / Gatasheh, Mansour K / Shahzad, Muhammad / Abbasi, Arshad Mehmood

Frontiers in plant science

2024 Volume 14, Page(s) 1268750

Abstract: Salinity is known to have a greater impact on shoot growth than root growth. ... ...

Abstract	Salinity is known to have a greater impact on shoot growth than root growth. Na
Language	English
Publishing date	2024-01-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2023.1268750
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Article: Aflatoxins in Wheat Grains: Detection and Detoxification through Chemical, Physical, and Biological Means.

Ismail, Ahmed Mahmoud / Raza, Muhammad Hassan / Zahra, Naseem / Ahmad, Rafiq / Sajjad, Yasar / Khan, Sabaz Ali

Life (Basel, Switzerland)

2024 Volume 14, Issue 4

Abstract: Wheat ( ...

Abstract	Wheat (
Language	English
Publishing date	2024-04-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662250-6
ISSN	2075-1729
ISSN	2075-1729
DOI	10.3390/life14040535
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Article ; Online: Teaching machines to optimizing machining parameters: using independent fuzzy logic controller and image data.

Mamledesai, Harshavardhan / Zheng, Yufan / Ahmad, Rafiq

SN applied sciences

2022 Volume 4, Issue 4, Page(s) 107

Abstract: Optimization of machining parameters like cutting speed, feed, and depth of cut is one of the extensively studied fields in the past two decades. While researchers agree optimization of these parameters is essential, there is no conscience as to what the ...

Abstract	Optimization of machining parameters like cutting speed, feed, and depth of cut is one of the extensively studied fields in the past two decades. While researchers agree optimization of these parameters is essential, there is no conscience as to what the objective of the optimization should be. The studies consider production cost, production time, surface finish, among others, as the objective of parameter optimization, but there are very few studies that consider the manufacturer prescribed tool life as the criteria for parament optimization. Among the methods that do consider tool life as an optimization objective, very few are closed-loop systems and these systems are facing challenges to generalizing when the application changes or the machining material changes or the tool geometry changes. Considering this, a novel image feedback using a convolution neural network-based method combined with principles of fuzzy logic is used to optimize machining parameters. Since the system is based on online feedback from the images of the inserts, it can be used for different materials, and the system is invariant to the different tool geometries and grades as the decisions are based on the wear mechanisms detected. The hybrid system is validated through experimentation for the turning application, but the methodology can be easily adapted for other machining applications.
Language	English
Publishing date	2022-03-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2947292-1
ISSN	2523-3971 ; 2523-3963
ISSN (online)	2523-3971
ISSN	2523-3963
DOI	10.1007/s42452-022-04987-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: The digitization of agricultural industry – a systematic literature review on agriculture 4.0

Abbasi, Rabiya / Martinez, Pablo / Ahmad, Rafiq

Smart agricultural technology. 2022 Dec., v. 2

2022

Abstract: Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world's population, agri-food demands are growing — posing the need to switch from traditional agricultural ... ...

Abstract	Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world's population, agri-food demands are growing — posing the need to switch from traditional agricultural methods to smart agriculture practices, also known as agriculture 4.0. To fully benefit from the potential of agriculture 4.0, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of agriculture 4.0 by investigating the emerging trends of digital technologies in the agricultural industry. For this purpose, a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses is conducted to analyse the scientific literature related to crop farming published in the last decade. After applying the protocol, 148 papers were selected and the extent of digital technologies adoption in agriculture was examined in the context of service type, technology readiness level, and farm type. The results have shown that digital technologies such as autonomous robotic systems, internet of things, and machine learning are significantly explored and open-air farms are frequently considered in research studies (69%), contrary to indoor farms (31%). Moreover, it is observed that most use cases are still in the prototypical phase. Finally, potential roadblocks to the digitization of the agriculture sector were identified and classified at technical and socio-economic levels. This comprehensive review results in providing useful information on the current status of digital technologies in agriculture along with prospective future opportunities.
Keywords	Internet ; agricultural industry ; farms ; food security ; meta-analysis ; robots ; socioeconomics
Language	English
Dates of publication	2022-12
Publishing place	Elsevier B.V.
Document type	Article
ISSN	2772-3755
DOI	10.1016/j.atech.2022.100042
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Feature extraction and process planning of integrated hybrid additive-subtractive system for remanufacturing.

Zheng, Yufan / Ahmad, Rafiq

Mathematical biosciences and engineering : MBE

2020 Volume 17, Issue 6, Page(s) 7274–7301

Abstract: Discussion regarding hybrid manufacturing has dominated research in recent years. By synergistically integrating additive and subtractive manufacturing within a single workstation, the relative benefits of each manufacturing strategy are leveraged. The ... ...

Abstract	Discussion regarding hybrid manufacturing has dominated research in recent years. By synergistically integrating additive and subtractive manufacturing within a single workstation, the relative benefits of each manufacturing strategy are leveraged. The ability to add, remove feature flexibly enables remanufacturing end-of-life components into a "new" part with new features and functionalities. However, in the remanufacturing context, the process planning for hybrid additive-subtractive manufacturing is still an unsolved research topic. In general, a hybrid remanufacturing process is signified by an alternating sequence of additive and subtractive operations that alternatively add and remove materials on a used part, which results in a non-unique process planning. For determining an optimal sequence for hybrid remanufacturing, a quantitative evolution mechanism is demanded. Moreover, the constraints in process planning are required to be considered. For example, the collision avoidance between the workpiece and the material-dispensing nozzle is one of the most critical limitations that affect the alternating sequence. To fill the gap, automated feature extraction and cost-driven process planning method for hybrid remanufacturing are proposed in this paper. The feature extraction, developed under the level set framework, can extract optimal and collision-free additive-subtractive features. Then, the hybrid process planning task is formulated into an integer programming model with cost estimations. A case study is conducted, and the results confirm the correctness and effectiveness of the proposed method.
Language	English
Publishing date	2020-12-28
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2265126-3
ISSN	1551-0018 ; 1547-1063
ISSN (online)	1551-0018
ISSN	1547-1063
DOI	10.3934/mbe.2020373
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