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Indonesia memiliki keanekaragaman hayati yang tinggi sehingga membuka peluang besar untuk diversifikasi pangan. Salah satu komoditi pangan alternatif yang berpotensi besar untuk dikembangkan adalah kacang koro dari genus Canavalia. Berbagai species Canavalia spp. memiliki kandungan gizi yang tinggi, potensi hasil yang tinggi (7-12 t/ha) dan digunakan sebagai bahan baku berbagai olahan pangan, obat, serta pakan ternak. Permasalahan dalam upaya pengembangan kacang koro adalah ketersedian lahan, sehingga penggunaan lahan marginal terpapar salinitas menjadi salah satu alternatif pilihan. Penelitian ini bertujuan untuk melihat respon agronomis dan fisiologis berbagai aksesi kacang koro yang ditanam pada lahan yang terpapar salinitas berbeda. Penelitian dalam bentuk percobaan dilaksanakan dari November 2022 hingga Maret 2024. Evaluasi karakter agronomis berbagai aksesi kacang koro Canavalia dilakukan pada tiga kondisi lahan dengan tingkat salinitas berbeda. Evaluasi di lahan non-salin berlangsung di kebun percobaan Fakultas Pertanian Universitas Andalas sedangkan evaluasi di lahan dengan tingkat salinitas sedang dan rendah dilakukan di Pantai Pasir Putih, Bungo Pasang, Kota Padang, Sumatra Barat. Observasi terhadap karakter fisiologis tanaman dilakukan di Laboratorium Fisiologi tumbuhan Fakultas Pertanian Universitas Andalas. Terdapat delapan aksesi species Canavalia spp. yang di evaluasi meliputi 2 aksesi dari spesies C. gladiata, 3 aksesi dari spesies C. ensiformis, 2 aksesi dari spesies C. maritima, dan 1 aksesi dari spesies C. cathartica. Karakter yang diamati meliputi karakter agronomis yaitu panjang batang, jumlah cabang, diameter batang, dan luas anak daun, kemudian karakter fisiologis yaitu ukuran stomata, kerapatan stomata, jaringan palisade dan kandungan prolin. Data dianalisis dengan analisis ragam gabungan, apabila uji F berbeda nyata dilanjutkan dengan uji lanjut DNMRT dengan bantuan aplikasi STAR (Statistical Tool for Agricultural Research). Berdasarkan komp

The early detection of both surface and subsurface damage is crucial for ensuring structural integrity. Timely repair of damage also delays the need for infrastructure replacement, which involves significant costs and has adverse environmental impacts. While manual inspection for damage detection is a common practice, it is expensive, time-consuming, and hazardous. Moreover, it cannot easily cover all structures. To enable safe and autonomous detection of surface and subsurface damage, an automated Structural Health Monitoring (SHM) system is required. In this thesis, deep learning-based methods are proposed for detecting external and internal damage in structures using computer vision and active thermography, respectively. Additionally, an automated SHM system was developed by integrating these deep learning-based SHM methods with autonomous flight capabilities of unmanned aerial vehicles (UAVs). For internal damage, a new internal damage segmentation network (IDSNet) was employed for pixel-wise subsurface damage segmentation. IDSNet comprises advanced deep learning operators such as the intensive module, residual intensive convolution module, and superficial module. These operators enable IDSNet to process large thermal images in real-time with high accuracy, reducing monitoring costs. To overcome the challenges of costly and time-consuming ground truth data collection, an attention-based generative adversarial network (AGAN) was developed to generate synthetic image data for training IDSNet. The IDSNet demonstrates superior performance compared to other networks in accurately segmenting internal damages using active thermography. In addition to subsurface damage, surface damage, such as pavement potholes, is of significant concern. This thesis introduces 3DPredicNet, a novel monocular deep learning-based method for pothole segmentation with 3D volume prediction. The 3DPredicNet incorporates an advanced attention mechanism to reduce the number of learnable paramet

The early detection of both surface and subsurface damage is crucial for ensuring structural integrity. Timely repair of damage also delays the need for infrastructure replacement, which involves significant costs and has adverse environmental impacts. While manual inspection for damage detection is a common practice, it is expensive, time-consuming, and hazardous. Moreover, it cannot easily cover all structures. To enable safe and autonomous detection of surface and subsurface damage, an automated Structural Health Monitoring (SHM) system is required. In this thesis, deep learning-based methods are proposed for detecting external and internal damage in structures using computer vision and active thermography, respectively. Additionally, an automated SHM system was developed by integrating these deep learning-based SHM methods with autonomous flight capabilities of unmanned aerial vehicles (UAVs). For internal damage, a new internal damage segmentation network (IDSNet) was employed for pixel-wise subsurface damage segmentation. IDSNet comprises advanced deep learning operators such as the intensive module, residual intensive convolution module, and superficial module. These operators enable IDSNet to process large thermal images in real-time with high accuracy, reducing monitoring costs. To overcome the challenges of costly and time-consuming ground truth data collection, an attention-based generative adversarial network (AGAN) was developed to generate synthetic image data for training IDSNet. The IDSNet demonstrates superior performance compared to other networks in accurately segmenting internal damages using active thermography. In addition to subsurface damage, surface damage, such as pavement potholes, is of significant concern. This thesis introduces 3DPredicNet, a novel monocular deep learning-based method for pothole segmentation with 3D volume prediction. The 3DPredicNet incorporates an advanced attention mechanism to reduce the number of learnable paramet

The aging and deterioration of bridge infrastructure is becoming a serious issue around the world. In this research, a new deep-learning-based method is proposed to detect subsurface damage in the steel elements of a bridge using thermography, without physical contact. Thermal images of the structural steel elements on the Arlington Bridge in Winnipeg, Manitoba, were captured using an uncooled microbolometer and were then used to train and validate a deep inception neural network; a maximum testing accuracy of 96% was achieved. Next, ultrasonic pulse velocity tests were conducted for validating thermal-image-based subsurface damage detection, and a contour map of the sections was plotted and compared with a deep-learning-based results. This research demonstrates that the combination of infrared thermal technology with a deep neural network is a practical approach to autonomously detecting subsurface damage in the elements of a steel bridge, with minimum human intervention.

The aging and deterioration of bridge infrastructure is becoming a serious issue around the world. In this research, a new deep-learning-based method is proposed to detect subsurface damage in the steel elements of a bridge using thermography, without physical contact. Thermal images of the structural steel elements on the Arlington Bridge in Winnipeg, Manitoba, were captured using an uncooled microbolometer and were then used to train and validate a deep inception neural network; a maximum testing accuracy of 96% was achieved. Next, ultrasonic pulse velocity tests were conducted for validating thermal-image-based subsurface damage detection, and a contour map of the sections was plotted and compared with a deep-learning-based results. This research demonstrates that the combination of infrared thermal technology with a deep neural network is a practical approach to autonomously detecting subsurface damage in the elements of a steel bridge, with minimum human intervention.

Activated carbon was prepared from date pits via chemical activation with H3PO4. The effects of activating agent concentration and activation temperature on the yield and surface area were studied. The optimal activated carbon was prepared at 450 °C using 55 % H3PO4. The prepared activated carbon was characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric-differential thermal analysis, and Brunauer, Emmett, and Teller (BET) surface area. The prepared date pit-based activated carbon (DAC) was used for the removal of bromate (BrO3 −). The concentration of BrO3 − was determined by ultra-performance liquid chromatography-mass tandem spectrometry (UPLC-MS/MS). The experimental equilibrium data for BrO3 − adsorption onto DAC was well fitted to the Langmuir isotherm model and showed maximum monolayer adsorption capacity of 25.64 mg g−1. The adsorption kinetics of BrO3 − adsorption was very well represented by the pseudo-first-order equation. The analytical application of DAC for the analysis of real water samples was studied with very promising results.

Activated carbon was prepared from date pits via chemical activation with H3PO4. The effects of activating agent concentration and activation temperature on the yield and surface area were studied. The optimal activated carbon was prepared at 450 °C using 55 % H3PO4. The prepared activated carbon was characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric-differential thermal analysis, and Brunauer, Emmett, and Teller (BET) surface area. The prepared date pit-based activated carbon (DAC) was used for the removal of bromate (BrO3 −). The concentration of BrO3 − was determined by ultra-performance liquid chromatography-mass tandem spectrometry (UPLC-MS/MS). The experimental equilibrium data for BrO3 − adsorption onto DAC was well fitted to the Langmuir isotherm model and showed maximum monolayer adsorption capacity of 25.64 mg g−1. The adsorption kinetics of BrO3 − adsorption was very well represented by the pseudo-first-order equation. The analytical application of DAC for the analysis of real water samples was studied with very promising results.

In this article the two phase heat transfer results of propane are presented. Experiments are performed up to dryout incipience in a circular mini channel made of stainless steel (AISI 316) having an internal diameter of 1.70 mm and a heated length of 245 mm. Experimental tests are done at a saturation temperature of 23 oC and for a heat flux range 5 - 240 kW/m2. Mass flux ranges from 100 to 400 kg/m2s. The effects of mass flux, heat flux and vapour quality on heat transfer coefficient are explored in detail. The results shows that the local heat transfer coefficients of propane are more or less independent of mass flux and vapour quality and is a strong function of heat flux until the occurrence of partial dryout. Finally the experimental results are compared with well known micro and macro scale correlations from the literature., QC 20120214

In this article the two phase heat transfer results of propane are presented. Experiments are performed up to dryout incipience in a circular mini channel made of stainless steel (AISI 316) having an internal diameter of 1.70 mm and a heated length of 245 mm. Experimental tests are done at a saturation temperature of 23 oC and for a heat flux range 5 - 240 kW/m2. Mass flux ranges from 100 to 400 kg/m2s. The effects of mass flux, heat flux and vapour quality on heat transfer coefficient are explored in detail. The results shows that the local heat transfer coefficients of propane are more or less independent of mass flux and vapour quality and is a strong function of heat flux until the occurrence of partial dryout. Finally the experimental results are compared with well known micro and macro scale correlations from the literature., QC 20120214

In this article the two phase heat transfer results of propane are presented. Experiments are performed up to dryout incipience in a circular mini channel made of stainless steel (AISI 316) having an internal diameter of 1.70 mm and a heated length of 245 mm. Experimental tests are done at a saturation temperature of 23 oC and for a heat flux range 5 - 240 kW/m2. Mass flux ranges from 100 to 400 kg/m2s. The effects of mass flux, heat flux and vapour quality on heat transfer coefficient are explored in detail. The results shows that the local heat transfer coefficients of propane are more or less independent of mass flux and vapour quality and is a strong function of heat flux until the occurrence of partial dryout. Finally the experimental results are compared with well known micro and macro scale correlations from the literature., QC 20120214

In this article the two phase heat transfer results of propane are presented. Experiments are performed up to dryout incipience in a circular mini channel made of stainless steel (AISI 316) having an internal diameter of 1.70 mm and a heated length of 245 mm. Experimental tests are done at a saturation temperature of 23 oC and for a heat flux range 5 - 240 kW/m2. Mass flux ranges from 100 to 400 kg/m2s. The effects of mass flux, heat flux and vapour quality on heat transfer coefficient are explored in detail. The results shows that the local heat transfer coefficients of propane are more or less independent of mass flux and vapour quality and is a strong function of heat flux until the occurrence of partial dryout. Finally the experimental results are compared with well known micro and macro scale correlations from the literature., QC 20120214

In this article the two phase heat transfer results of propane are presented. Experiments are performed up to dryout incipience in a circular mini channel made of stainless steel (AISI 316) having an internal diameter of 1.70 mm and a heated length of 245 mm. Experimental tests are done at a saturation temperature of 23 oC and for a heat flux range 5 - 240 kW/m2. Mass flux ranges from 100 to 400 kg/m2s. The effects of mass flux, heat flux and vapour quality on heat transfer coefficient are explored in detail. The results shows that the local heat transfer coefficients of propane are more or less independent of mass flux and vapour quality and is a strong function of heat flux until the occurrence of partial dryout. Finally the experimental results are compared with well known micro and macro scale correlations from the literature., QC 20120214

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