Technological University Research Articles

VHL mutation drives human clear cell renal cell carcinoma progression through PI3K/AKT-dependent cholesteryl ester accumulation

Cholesteryl ester (CE) accumulation in intracellular lipid droplets (LDs) is an essential signature of clear cell renal cell carcinoma (ccRCC), but its molecular mechanism and pathological significance remain elusive. Enabled by the label-free Raman spectromicroscopy, which integrated stimulated Raman scattering microscopy with confocal Raman spectroscopy on the same platform, we quantitatively analyzed LD distribution and composition at the single cell level in intact ccRCC cell and tissue specimens in situ without any processing or exogenous labeling. Since we found that commonly used ccRCC cell lines actually did not show the CE-rich signature, primary cancer cells were isolated from human tissues to retain the lipid signature of ccRCC with CE level as high as the original tissue, which offers a preferable cell model for the study of cholesterol metabolism in ccRCC. Moreover, we established a patient-derived xenograft (PDX) mouse model that retained the CE-rich phenotype of human ccRCC. Surprisingly, our results revealed that CE accumulation was induced by tumor suppressor VHL mutation, the most common mutation of ccRCC. Moreover, VHL mutation was found to promote CE accumulation by upregulating HIFα and subsequent PI3K/AKT/mTOR/SREBPs pathway. Inspiringly, inhibition of cholesterol esterification remarkably suppressed ccRCC aggressiveness invitro and invivo with negligible toxicity, through the reduced membrane cholesterol-mediated downregulations of integrin and MAPK signaling pathways. Collectively, our study improves current understanding of the role of CE accumulation in ccRCC and opens up new opportunities for treatment. This work was supported by National Natural Science Foundation of China (No. U23B2046 and No. 62027824), National Key R&D Program of China (No. 2023YFC2415500), Fundamental Research Funds for the Central Universities (No. YWF-22-L-547), PKU-Baidu Fund (No. 2020BD033), Peking University First Hospital Scientific and Technological Achievement Transformation Incubation Guidance Fund (No. 2022CX02), and Beijing Municipal Health Commission (No. 2020-2Z-40713).

Mastering the art of technical writing in it: Making complex things easy to understand in Atate campus

This research, conducted at the Atate Campus of Nueva Ecija University of Science and Technology, delves into the realm of technical writing within the Information Technology (IT) sector, focusing on the pedagogical strategies that facilitate the comprehension of complex technical topics by IT students. It seeks to uncover the challenges IT students face when engaging with technical documentation and the effective methods to surmount these obstacles. Through a qualitative and quantitative analysis involving a survey distributed to 100 IT students across various academic years, the study identifies significant challenges such as conceptual complexity, jargon prevalence, and the need for audience-tailored documentation. The research highlights best practices like the use of plain language, visuals, real-world examples, and the critical role of user testing in enhancing document clarity and understandability. Furthermore, it examines preferences in writing styles and document structures, offering insights into effective technical communication tailored to diverse audience needs. This study proposes a set of recommendations aimed at improving technical writing curriculum and pedagogy, encouraging future research, and fostering a deeper understanding of technical subjects among IT students through enhanced technical writing skills.

In vitro Evaluation of Botanical Extracts against Mycelial Growth of Alternaria solani

An experiment was conducted at the laboratory of the Department of Plant Pathology, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, Uttar Pradesh. To test the efficacy of different treatments viz., Neem (Azadirachta indica), Onion (Allium cepa), Garlic (Allium sativum), Castor (Ricinus communis), Amaltas (Cassia fistula), Henna (lawsonia inermis), Madar (Calotropis gigantea) against Alternaria solani under in vitro condition. Botanicals were tested through the poisoned food technique at 5% and 10 % concentrations and 168 hours of incubation. The Minimum radial growth of observed in  Neem (Azadirachta indica) seed kernel extract @ 5% (14.8mm) followed by Henna (lawsonia inermis) leaf extract @ 10% (43.36mm), Amaltas (Cassia fistula) leaf extract @ 10% (67.13mm),  Castor (Ricinus communis) leaf extract @ 10% (67.6mm), Madar (Calotropis gigantea) leaf extract @ 10% (69.6mm), Onion (Allium cepa) extract @ 10 % (70.8mm), Garlic (Allium sativum) extract @ 10 % (72.2mm) as compared to Control (89.4mm) untreated check.

Perfect Polar Alignment of Parallel Beloamphiphile Layers: Improved Structural Design Bias Realized in Ferroelectric Crystals of the Novel "Methoxyphenyl Series of Acetophenone Azines".

Invited for the cover of this issue are Harmeet Bhoday, Nathan Knotts, and Rainer Glaser at the Missouri University of Science and Technology. Joe Miner, the university mascot, is a silent and honorary co-author and personifies the spirit of the old west and the determination to succeed (our emphasis). The image depicts one (MeO-Ph, Y)-azine molecule and a model of a perfectly polar stacked bilayer. The floodlight illumination presents a graphic metaphor of second-harmonic generation (frequency doubling) by the crystalline ferroelectric materials. Read the full text of the article at 10.1002/chem.202400182.

ALTASN: A few-shot learning fault diagnosis method for rotating machinery of unmanned surface vehicles based on attention mechanism

Rotating machinery is one of the principal power equipment of unmanned surface vehicles (UVs). Considering the complex and harsh offshore working conditions of UVs, the health status of rotating machinery is highly to be affected, but it is often difficult to obtain enough fault samples. Accordingly, limited data fault diagnosis of rotating machinery holds great practical significance to increase the resilience and security of UVs. For limited data fault diagnosis, a novel few-shot learning model based on attention mechanism called adaptive long-term attention siamese network (ALTASN) is proposed. First, an efficient channel attention mechanism is combined with adaptive convolutional kernels to improve the spatial feature extraction capabilities of the convolutional neural network (CNN). To capture and assign higher weights to important long-term dependent information, long-term attention is introduced to improve the ability of long short-term memory networks (LSTM) temporal feature extraction. Finally, the siamese network is introduced to compare the features of different sample pairs to obtain the final fault type. In the case of limited data, the fault diagnosis performance and generalization ability of the proposed ALTASN are better compared with existing results. Experiments are carried out on the actual three-phase asynchronous motor experiment platform at the Zhejiang University of Technology to verify the effectiveness and generalization of the proposed method.

EngiWave: Engineering College Recommendation System using Decision Tree and Random Forest Algorithm

Students face numerous challenges when trying to enroll in their desired institution, particularly in engineering programs. The admissions process is complex, often resulting in students being accepted into lower-ranked institutions than they deserve. To address this, the Council Entrance Predictor utilizes historical acceptance data to forecast the most likely institutions for students. Engiwave, another tool, employs similar data to predict potential institutions based on academic performance, background, and admission criteria. These predictions are facilitated by a machine learning model trained on a dataset containing student profiles and university details, including admission outcomes. By leveraging machine learning and database techniques, the system provides students with a prioritized list of engineering institutions where they have the highest probability of admission, aiding them in preparing their applications effectively.

National Agricultural Innovation System (NAIS): Diagnosis, Gaps, and Mapping of Actors

This article examines the diagnosis of the Agricultural Innovation System (SIA) in the department of Tolima, Colombia, as part of a project led by the Colombian Ministry of Agriculture and Rural Development and the Technological University of Pereira (TUP), which also evaluated the National Agricultural Innovation System (NAIS) in regions such as Chocó, Bolívar, and Meta. A methodology was used that involved a survey with 58 questions on a Likert scale with 300 participants, evaluated with congruence and relevance indexes, identifying key variables in the operation of the Agricultural Innovation System (AIS). A survey was applied to map actors in the National Innovation System for Higher Education in Agriculture, as well as the participating actors in the regional agricultural innovation systems of Tolima to identify gaps in extension, innovation, development, sustainability, information technology (TI), education, and training. Among the conclusions, it stands out that the associativity subsystem presents the lowest performance and is close to a score of two on the scale of gaps. An evaluation was carried out among the five subsystems, including extension, TI, environmental aspects, and public policy. The department of Tolima shows low performance in innovation and TI, while their extension performance is less weak. In Chaparral, there is a general lag, with innovation being the worst evaluated subsystem. Rovira obtains high scores in most subsystems, although innovation and TI have lower scores. At the national and regional levels, innovation is low, and communication among the NARS actors is insufficient, which highlights the need to promote good practices, strategies, and projects.

A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network.

For the brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is difficult to obtain satisfactory classification performance for short-time window SSVEP signals by traditional methods. In this paper, a fused multi-subfrequency bands and convolutional block attention module (CBAM) classification method based on convolutional neural network (CBAM-CNN) is proposed for discerning SSVEP-BCI tasks. This method extracts multi-subfrequency bands SSVEP signals as the initial input of the network model, and then carries out feature fusion on all feature inputs. In addition, CBAM is embedded in both parts of the initial input and feature fusion for adaptive feature refinement. To verify the effectiveness of the proposed method, this study uses the datasets of Inner Mongolia University of Technology (IMUT) and Tsinghua University (THU) to evaluate the performance of the proposed method. The experimental results show that the highest accuracy of CBAM-CNN reaches 0.9813 percentage point (pp). Within 0.1-2s time window, the accuracy of CBAM-CNN is 0.0201-0.5388 (pp) higher than that of CNN, CCA-CWT-SVM, CCA-SVM, CCA-GNB, FBCCA, and CCA. Especially in the short-time window range of 0.1-1s, the performance advantage of CBAM-CNN is more significant. The maximum information transmission rate (ITR) of CBAM-CNN is 503.87bit/min, which is 227.53bit/min-503.41bit/min higher than the above six EEG decoding methods. The study further results show that CBAM-CNN has potential application value in SSVEP decoding.

A vision of a more sustainable Slovakia in the light of participatory processes

The paper reflects the results of the ongoing discussion about sustainable development in Slovakia as an increasingly urgent topic resonating across scientific disciplines. Specifically, it deals with the participation processes, which were realized in 2022-2023 under the auspices of the Ministry of Investments, Regional Development and Informatization of the Slovak Republic (MIRRI), and the second part of which was implemented at the Institute of Management of the Slovak University of Technology in Bratislava using its expert and organizational capacities. The mentioned activities follow the strategic document entitled Vision and Development Strategy of Slovakia 2030 and are related to the preparation of the document Vision and Development Strategy of the Slovak Republic until 2050 – Slovakia 2050. In the light of the outputs from the participation processes, the paper deals, among other things, with the necessity of accelerating the transformation of regional economies from the industrial towards the post-industrial society, including sustainable innovation-oriented circular economy less dependent on raw materials and energy, with the simultaneous optimization of the system of decision-making and effective management, based on partnership and division of responsibilities between different levels and various actors of sustainable development. It also points out that discussions about this vision have the potential to significantly frame the creation of concepts of integrated territorial innovation-oriented clusters, the urgency of which - in parallel with the frustration of the ineffectiveness of fragmented development processes and activities - is felt at different levels by practically all participating subjects of the professional discourse analyzed in this paper. The results of the participation process fully confirm that Slovakia is not adequately prepared to respond to the challenges associated with all the necessary transformation processes.

Biodiversity and Foraging Behaviour of Major Insect Pollinators on Some Fruit Crops

Beekeeping entirely depends on the types of flowering plants available in any given area. The honey bees (Apis species) have a major role and are considered widely as pollinating agents. The present study was conducted at Govind Ballabh Pant University of Agriculture and Technology, Pantnagar to find out the biodiversity and foraging behaviour of major insect pollinators on some fruit crops. The honey bees (Apis species) have major role and considered widely as pollinating agents. In most of the crops we largely seek for honey bees and depend on them for pollination services.

Effect of Nitrogen and Crop Geometry on Growth and Yield of Baby Corn

The field experiment was conducted to investigate the Effect of Nitrogen and Crop Geometry on Growth and Yield ofBaby Corn during Zaid season of 2023 at the Crop Research Farm, Department of Agronomy, Naini Agricultural Institute, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj (U.P.) India. The main objectives of experiment is to studythe efficacy of nitrogen and crop geometry on growth and yield of baby corn. The experiment was executed using Randomized Block Design with ten treatments and replicated thrice. The treatments combinations are T1:Nitrogen 60kg/ha + Crop Geometry 40cm × 20cm T2: Nitrogen 80kg/ha + Crop Geometry40cm × 20cm T3: Nitrogen 100kg/ha + Crop Geometry 40cm × 20cm T4: Nitrogen 60kg/ha + Crop Geometry 50cm × 20cm T5: Nitrogen 80kg/ha + Crop Geometry 50cm × 20cm T6: Nitrogen 100kg/ha + Crop Geometry 50cm × 20cm T7: Nitrogen 60kg/ha + Crop Geometry 60cm × 20cm. T8: Nitrogen 80kg/ha + Crop Geometry 60cm × 20cm T9: Nitrogen 100kg/ha + Crop Geometry 60cm × 20cm, T10: Control (RDF-60- 40-40 NPK kg/ha) are used. The result of the study revealed that the higher plant height (173.47 cm), higher plant dry weight (29.3 g/plant), number of cobs/plant (2.1), green fodder yield (33.32 t/ha), cob yield with husk (12.30 t/ha), andcob yield without husk (4.33 t/ha) were significantly showed significant difference with application of Nitrogen100kg/ha + Crop Geometry 50cm × 20cm. It was concluded that for obtaining higher yield components with better quality of baby corn with Nitrogen 100 kg/ha + Crop Geometry 50cm × 20cm was recommended.

Recurrent Neural Network Research on a Flipped Classroom Online Collaborative Teaching Platform

A Recurrent Neural Network (RNN) is a type of artificial neural network designed to effectively process sequential data by maintaining a memory of past inputs. A Collaborative Online Teaching platform is a digital space designed to facilitate interactive and engaging learning experiences between educators and students in virtual environments. It integrates various tools and features to enable real-time communication, content sharing, and collaborative activities. The collaborative features such as breakout rooms, group projects, and interactive whiteboards promote teamwork and active participation among students. Assessment tools within the platform facilitate the evaluation of student progress through quizzes, assignments, and grading functionalities. This research investigates the integration of Problem Based Learning (PBL) and cooperative instructional methods within flipped classrooms, aiming to examine their impact on students' intrinsic motivation to learn. The study endeavors to develop a tutorial strategy aligned with the concept of "flipped schools" to enhance students' self-directed study, problem-solving abilities, academic achievement, and interest in instruction. First-year students from Southern Taiwan College of Science and Technology's School of Information Management participated, focusing on education in C#. The participants were divided into a trial group, experiencing the proposed teaching tactics, and an identical control group with randomly selected groups. Pre- and post-test scores from computing classes were analyzed to assess learning outcomes. Additionally, a survey explored students' perceptions of the flipped classroom approach, project-based schooling, and collaborative learning. Recurrent Neural Network (RNN) processes were employed to analyze sequential data, such as student performance over time and engagement patterns. Results indicate that the recommended instructional strategies led to improved comprehension levels, with the experimental group showing significantly greater progress compared to the control. 

Research on Innovation and Entrepreneurship Education Reform of Materials Science and Engineering in Local Applied University

Research on Innovation and Entrepreneurship Education Reform of Materials Science and Engineering in Local Applied University

The temporal development of teachers’ attitudes on entrepreneurship education – A latent growth curve analysis

This research explores the attitudes of teachers working in Finnish Universities of Applied Sciences (UAS) on the importance and interest of entrepreneurship education and the stability of those attitudes in three years. A longitudinal data of three-waves consists of 204 answers from Finnish UAS teachers. Latent growth curve modelling reveals notable variations in the attitudes among individuals and their long-term stability. Prior entrepreneurship-related training and perceived university support have a positive impact on teachers' attitudes, contributing to their formation, and attitudes tend to persist over time. Our results emphasize the importance of early career support and entrepreneurship training for teachers.

Empowering architecture, engineering and construction students through building information modeling competitions: a deep dive into behavioral motivation

PurposeBuilding Information Modeling (BIM) competitions are a beneficial approach to enhance BIM education, offering students practical experience in BIM application, including mastering workflows and technical tools. However, research exploring the individual perceptions influencing participation intentions and behaviors in BIM competitions is limited. Therefore, this study aims to investigate the factors affecting university students' behavioral intention and behavior in BIM competitions, providing theoretical support for BIM competitions and educational reform.Design/methodology/approachThis study employs the Structural Equation Modeling (SEM) based on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to analyze the factors influencing BIM competition participation among 970 Architecture, Engineering, and Construction (AEC) university students.FindingsThe results of the study show that social influence, attitude, and self-efficacy play critical roles in shaping students' intentions to participate in BIM competitions. Furthermore, self-efficacy, facilitating conditions, and behavioral intention significantly influence students' actual engagement in such competitions. Surprisingly, effort expectancy negatively influences intentions, as less challenging tasks can lead students to perceive their participation as less impactful on their skills and learning, reducing their behavioral intention to participate.Originality/valueThis research provides valuable insights into the effectiveness of BIM competitions in enhancing BIM education for AEC students. Extending the UTAUT model to include self-efficacy and attitude, provides a novel perspective for understanding students' intentions and behaviors regarding BIM competitions. The study’s theoretical support proposes incorporating BIM competitions to augment BIM teaching methods and offers suggestions for advancing the efficacy of students' involvement in BIM competitions within higher education, thus contributing to educational reform in the AEC sector.

Screening of Lentil Genotypes Against Wilt of Lentil (Lens culinaris Medik L.) Caused by Fusarium oxysporum F. SP. Lentis in Glasshouse Condition

Wilt of lentil is most damaging disease of lentil caused by the fungus Fusarium oxysporum f. sp. lentis. It causes severe crop damage from vegetative to reproducing stage which results in yield reduction. Fol isolates shows high variability and aggressiveness according to the climatic condition. So to overcome from this disease there are many ways of management like by cultural, mechanical, biological and chemical practices. Resistant variety selection is one of the best ecofriendly cultural management of the disease. So for this purpose one hundred genotypes were taken and screened through pot screening technique under glasshouse was carried out at the Student Instructional Farm (SIF), in the department of Plant Pathology, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, which is situated in the IndoGangatic plains of eastern Uttar Pradesh at a latitude of 26.470 and an altitude of 113 meters above sea level during Rabi 2020-21. One hundred genotypes of lentil were obtained from the Department of Genetics and Plant breeding, N.D. University of Agriculture and Technology, Kumarganj, Ayodhya and IIPR, Kalyanpur, Kanpur (U.P.).Out of 100 genotypes, 41 genotypes were found highly resistant, 30 genotypes resistant, 8 genotypes moderately resistant, 10 genotypes susceptible and 11 genotypes highly susceptible to Fusarium wilt.

<span>Development and evaluation of digitally managing university extracurricular activities</span>

Assessment and Grading website has been developed using a web-based platform, designed to facilitate the evaluation of extracurricular activities related to students’ academic performance at Cantho University of Technology. This approach has allowed the university to streamline its grading process with a centralized online application, encompassing all essential contingencies, including the consolidation, storage, and retrieval of transcripts from previous academic years, along with supporting evidence of student activities. This has resulted in increased transparency and flexibility in terms of time and location for processing. Users have highly praised the website's contribution and its supporting role, with satisfaction levels exceeding 90% across various criteria: (1) they highly satisfied with the website's utility and convenience. (2) The website has improved grading and review processes, reduced printing costs, facilitated document retrieval, allowed for the comparison of student activity evidence, optimized storage space, and streamlined procedures. (3) It has played a crucial role in enhancing accuracy and transparency in student grading, significantly contributing to the digital transformation efforts, making the management of student extracurricular activities at the university more essential and efficient.

Customizable Presentation Attack Detection for Improved Resilience of Biometric Applications Using Near-Infrared Skin Detection

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device’s modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.

Educators’ Academic Insights on Artificial Intelligence: Challenges and Opportunities

The study on " Educators’ Academic Insights on Artificial Intelligence – Challenges and Opportunities" was conducted to gain a deeper understanding of the rapidly evolving phenomenon of AI in education. This research serves multiple objectives. Firstly, it aims to foster awareness regarding the integration of AI into teaching and learning practices by providing clear definitions of AI and explaining key AI-related terms. It also seeks to illustrate AI's diverse applications within a broader context, with a special focus on AI-supported research and learning platforms. Additionally, the study delves into the current discourse surrounding chatbots, contributing to address the central research question. Lastly, this initiative aims to provide valuable recommendations for effectively harnessing AI in education, enhancing the teaching and learning experience. The researchers conducted a review of literature concerning artificial intelligence. They adopted a qualitative method, using open-ended questions to collect feedback from educators globally, including those from the University of Technology and Applied Sciences, Al Musannah, and participants in the online discussion forum at Oxford English Learning Exchange.com. The qualitative data was analysed, leading to the identification of key themes and subthemes derived from the responses of research participants. The study's findings incorporated a wide range of concerns expressed by educators, comprising ten key subthemes. These concerns ranged from doubts about AI's ability to replace human educators and fears of its potential to hinder student development to worries about its hyped popularity and its perceived futuristic nature. Educators stressed the importance of effective AI training while emphasizing the need to prioritize human expertise over excessive reliance on AI. They were also acutely aware of both the advantages and disadvantages of AI, viewing it as both a potential boon and a looming threat. Furthermore, educators recognized the potential for enjoyable experiences with AI and acknowledged the pivotal role of users in determining the extent of AI adoption. Content analysis revealed additional apprehensions, such as concerns about job displacement, AI's impact on critical thinking, teacher frustration in assessing AI-assisted student writing, the use of AI-generated content for assessments, potential erosion of human services, stifling of user and learner creativity by AI, the risk of errors in AI-generated information, opportunities for cheating in exams, and concerns about the overreliance on and overrating of AI platforms. Positively, the findings included an array of opportunities that AI platforms offer. Study participants highlighted various aspects of these opportunities that surpassed their concerns and associated risks. The opportunities are categorized into twenty subthemes: enhancing learner motivation, facilitating template creation, utilizing AI as an educational aid, promoting proper training and fostering positive AI usage, harnessing AI for teaching challenging subjects, enabling personalized learning experiences, offering an interactive tutoring experience, supporting remote learning, facilitating self-study, providing comprehensive educational content overviews, giving instantaneous feedback and evaluation, functioning as search engines and chatbots, enabling content validation, efficiency in terms of cost and time, streamlining material preparation, facilitating skill and language enhancement, promoting familiarity with topics and vocabulary, enabling text-to-speech and speech-to-text conversions, editing multimedia elements, and facilitating content generation.

Optimization of Crop Establishment Methods and Phosphorus Fertilizer Levels on Growth and Economic Efficiency of Groundnut under Semi-Arid Region of Afghanistan

An experiment was conducted at the farm of Afghanistan National Agricultural Science and Technology University (ANASTU), Kandahar Province, Afghanistan in cropping season of 2020 to investigate the Optimization of Crop Establishment methods and Phosphorus Fertilizer levels on Growth and Economic Efficiency of Groundnut under Semi-arid region of Afghanistan. The experimentation was conducted in split-plot design with 15 treatment combinations and replicated thrice. main-plot consisted of crop establishment methods, viz. ridge and furrow (RF), broad bed and furrow (BBF) and flatbed (FB), while the sub-plots comprised of phosphorus levels, viz. absolute control, 20, 40, 60 and 80 kg P2O5/ ha. The results of the investigation revealed that the growth parameters in terms of plant height in and leave area were highest in BBF, followed by FB and minimum was in RF. Adoption of BBF recorded significantly higher gross return (238928 AFN/ha), net return (202728 AFN/ha) and net benefit cost of ratio (5.2) were in Broad Bed and Furrow, followed in ridged and furrow and the minimum was in flat bed method. It can be concluded, cultivating of groundnut with Broad Bed and Furrow with application of 60 kg P2O5/ha was found beneficial for reaching higher productivity and profitability under semi-arid region of Afghanistan.