Miracle-dz/UniFAR-FTUs huggingface dataset:
Farmer Assistant System: An Integrated Crop Disease Detection and Remedial Approach

This abstract introduces a crop disease prediction and production management system employing deep learning techniques. With the growing demand for crops, ensuring their healthy cultivation is crucial. This system combines advanced deep learning algorithms with agricultural data to predict and manage crop diseases. By analyzing various factors such as weather conditions, soil quality, and historical disease occurrences, the system accurately forecasts potential disease outbreaks. This predictive capability empowers farmers to take proactive measures, minimizing crop losses and reducing the need for excessive chemical treatments. Additionally, the system aids in production management by optimizing irrigation, fertilizer application, and harvest scheduling, enhancing overall yield and quality. Through the fusion of deep learning and agricultural expertise, this innovative system contributes to sustainable crop cultivation, benefiting both farmers and consumers. In this paper we have tried to work on all the above-mentioned ideas and explore the scope and discuss the useful of this project in relevance to today’s needs with the help of a full-stack website using PHP, Bootstrap, MySQL and a Python library known as the Streamlit.
Consumer Devices for Health Parameter Collection and Analysis in Parkinson's Disease Telerehabilitation

Physical exercise is a significant non-pharmacological approach for individuals with Parkinson's disease (PD) to improve their condition. In this paper, a remote monitoring system for physical exercises has been proposed to enhance patients' rehabilitation. Wearable devices are utilized to collect health parameters throughout the day. These parameters are then stored on a remote server, facilitating subsequent analysis. The analysis employs techniques such as the Tukey test, PCA technique, and K-means clustering algorithm. The primary objectives of this analysis are twofold: first, to identify any changes in the patients' health status over the monitoring period, and second, to assess the acceptability of the system. By analyzing the collected data, the analysis aims to detect patterns, trends, and significant variations in the health parameters, providing valuable insights into the patients' progress. Additionally, it serves to evaluate the effectiveness and practicality of the system, ensuring its acceptability for both patients and healthcare providers involved in the rehabilitation process.
Assessment of Wearable Lower Limb Rehabilitation Robot Dynamics: A Study on ROM Training Performance

A wearable lower limb rehabilitation robot (WLLR) is one of the effective ways to assist a stroke patient who has abnormal gait. The accuracy of the dynamic model is important as it relates to the performances of the robot to track the trajectory. Error in modelling can be magnified futher resulting to poor tracking performance. However, the development of the dynamic model for WLLR is challenging because the structure is highly non-linear and heavily coupled. In this paper, a mathematical modelling for an improved design of wearable lower limb rehabilitation robot (WLLR) is presented. The Lagrangian formulation was utilized to derive the dynamic model of the hip and knee joint. Simple PID (Ziegler–Nichols) based controller was developed in order to verify the developed dynamic model. MATLAB SimMechanic software was used to simulate the WLLR motion behaviour to imitate a real environment. The results demonstrate the successful tracking of desired ranges of motion (ROM) by WLLR joints with low rise time and steady-state error. Thus, the developed dynamic model is acceptable and can be utilized for future improvement of the controller systems for WLRR.
Análise comparativa de topologias de redes coletoras de parques eólicos offshore para sistemas isolados

A expansão da geração de energia eólica offshore no mundo tem motivado o aumento das pesquisas e estudos relacionados a esse tipo de sistema para aplicação no Brasil. Parques eólicos offshore podem possuir diferentes configurações. Esse artigo dá uma visão geral da estrutura de um parque eólico offshore, apresenta diferentes layouts para as redes coletoras desses sistemas, focando na aplicação em sistemas isolados. O artigo apresenta um método para análise preliminar comparativa das perdas elétricas para as diferentes configurações de redes coletoras, demonstrando sua aplicação.
A Comparative Study of SAM’s Performance Against Three U-Net Architectures on Retinal Vessels Segmentation Task

Semantic Segmentation is a pivotal undertaking within the domain of medical imaging analysis. It involves partitioning of a medical image into distinct regions of interest. Many deep learning architectures has led to the automation of the segmentation task, yielding performance levels close to those of domain experts. The U-Net architecture and many of its variants, has showcased their effectiveness for this task especially for biomedical images. Recently, the Segment Anything Model (SAM) has been released showing impressive performance in segmenting natural images. This paper is about challenging SAM on the task of retinal vessels image segmentation in comparison of three U-Net architectures, namely vanilla U-Net, Attention U-Net, and Spatial Attention U-Net. The evaluation is conducted on five diverse retinal vessel datasets. Our findings reveal that the inference mode of the SAM model, based on zero-shot learning, demonstrates inferior segmentation performance. However, when fine-tuned, it surpassed the performance of the U-Net models across all the studied datasets.
Revolutionizing University Graduate Employability: Leveraging Advanced Machine Learning Models to Optimize Campus Recruitment and Placement Strategies

In the competitive landscape of the job market, universities are faced with the challenge of not only providing quality education but also ensuring the successful placement of their graduates into the workforce. The use of advanced machine learning models offers a promising solution to this challenge by providing universities with the ability to efficiently analyse vast amounts of data and identify patterns that can be used to optimize their recruitment and placement strategies. In this paper, we delve into the exciting world of machine learning and explore how it can revolutionize university graduate employability. From predictive models that analyse student performance, interests, and career aspirations to identify the best-fit job opportunities for each student, to machine learning algorithms that match job candidates with suitable job openings based on their skills, experience, and qualifications, the possibilities are endless. It is essential for universities and companies to implement responsible machine learning practices and ensure that their algorithms are fair and unbiased to prevent issues of bias and discrimination. As the job market becomes more competitive and complex, universities and companies must leverage advanced technologies to remain competitive and attract top talent. By embracing machine learning and developing responsible machine learning practices, universities can optimize their recruitment and placement strategies and enhance the employability of their graduates. The use of advanced machine learning models presents an exciting opportunity for universities to optimize their recruitment and placement strategies and revolutionize university graduate employability.
Delving Into the Security & Privacy of the Metaverse Matrix

In the burgeoning Metaverse, an intricate digital realm sculpted by the convergence of physical virtual reality and enduring virtual spaces, there lies unprecedented potential for revolutionary social and economic interactions. However, despite its growing influence in our daily lives, there exists a notable research gap: a comprehensive understanding of the security and privacy facets inherent to the Metaverse. This study endeavors to bridge this gap by examining the pivotal dimensions of security and privacy within this nascent domain. As the distinctions between physical and digital realms fade, comprehending and addressing vulnerabilities becomes vital to preempt threats and uphold individual freedoms. Our findings elucidate not only the continuous threats, such as unauthorized data breaches and virtual reality hardware susceptibilities but also spotlight promising countermeasures. The utility of blockchain for decentralized identity protection and zero-knowledge proofs for enhancing transactional privacy without excessive data exposure are exemplary solutions. Additionally, the emergence of AI-driven security offers the potential for defenses that adapt to shifting threats. Yet, relying exclusively on technological innovations is insufficient. The Metaverse's enduring success and safety rest on a symbiotic relationship among technology, regulatory frameworks, and informed user participation. This requires developers to be proactive in integrating security, regulators to formulate and enforce robust standards, and users to stay vigilant. To conclude, the Metaverse, with its transformative potential, can only flourish within a trustworthy and secure environment. It necessitates a collaborative effort from all stakeholders to foster a Metaverse that truly enriches, empowers, and connects users in this expansive digital landscape.
Exploring Student Involvement in E-Learning

Student involvement in e-learning is a complex concept that includes emotional, behavioral, cognitive, social, and contextual dimensions. It plays a crucial role in enhancing students' satisfaction and success. However, identifying motivated learners can be difficult for educators, especially in online environments that lack physical interaction. This challenge can make it difficult to tackle and minimize dropout rates. This study presents an innovative approach to predicting involvement levels in online education and examines the correlation between student success and motivation. This study was conducted using interaction data from 1,356 learners in e-learning courses over winter from 2020 to 2022. When constructing a model for involvement, different factors were considered, including the number of posts in forums and cumulative time spent on the e-learning platform. Emotions within student discussions were identified using the BiLSTM method, in conjunction with FastText word embedding. Subsequently, an unsupervised clustering approach was applied to categorize the learners according to their motivation levels. Various classification algorithms were tested and assessed for their efficacy using cross-validation and multiple accuracy metrics. The research identified the decision tree rule model as the best fitting, with a remarkable accuracy rate of 98% and an AUC score of 0.97. The findings also underline that most learners are more observers than active participants, revealing a sophisticated, nonlinear relationship between learning success and involvement.
Assistive Device for Differently Abled People

It is difficult to focus on and solve the issues experienced by persons with varied abilities, such as those who are visually, audibly, or vocally handicapped, using just one technology. Lastly, based on the Raspberry Pi microcontroller and a few more parts, this research proposes an assistive gadget for those who are blind, deaf, and dumb. Raspberry Pi as the main microcontroller, a touch screen display for being user friendly, a Bluetooth microphone that uses the voice bot app, an earphone for the voice output Pi camera for object detection, UART and HC 05 as Bluetooth modules, and an ultrasonic sensor for obstacle detection are all included in the system. We are mainly focusing on six important objectives in this paper which include: Speech to text, text-to-speech using Google API, image- to-speech, face recognition to save and recognize photographs of close relatives in both voice and text, obstacle detection, and gesture recognition are the device’s six main goals. The gadget is based on the Raspberry Pi and makes use of its capabilities and computing power. It also has a 3.5- inch touchscreen for user- friendliness. The system’s elements work together to produce a complete and simple aid for people with disabilities. The device’s performance, advantages, and possibilities for advancement in the future are all thoroughly examined.
Rate Comparison of Two-User Network Applying Various Multiple-Access Techniques

In multi-user wireless cellular networks, transmission of information can be on orthogonal basis or non-orthogonal basis. Both types of medium access has advantages and disadvantages. The main challenge of orthogonal methods is the inefficient utilization of the spectrum while non-orthogonal methods are challenged by interference. Effect of interference in non-orthogonal multiple access (NOMA) methods can be reduced if some of users apply successive interference cancellation (SIC). In this work we consider the two-user case and investigate the rate performance assuming non-fading regime. The aim is to identify use cases where orthogonal multiple access (OMA) outperform NOMA and vice versa. We also consider various cases for applying SIC in NOMA. We relied on analytical methods, and to lesser extent on simulation, to come with some conclusions about rate performance. Although the assumed model is simple, it gives useful insight to understanding these two types of multiple access and related trade-offs. The assumed model is also important in acting as the base model used for conducting further studies.
Deep Learning Applied to the Measurement and Verification of Photovoltaic Projects

With the aim of reducing the monitoring period from twelve to six months, this article presents an approach that utilizes Long Short-Term Memory (LSTM) neural networks to predict solar irradiance. This methodology plays a crucial role in measurement and verification campaigns in energy efficiency projects, particularly those focused on solar generation, where solar irradiance is an essential factor. The evaluation of this method in photovoltaic system projects has yielded satisfactory conclusions, offering a viable alternative for measurement and verification in energy efficiency projects.
Mit Lichtstrahlen das Gehör wiederherstellen Restoring Hearing with Light

: When hearing fails, cochlear implants (CIs) provide most CI users with open speech understanding in quiet environments. CIs bypass the defective sensory organ and electrically stimulate the auditory nerve. The bottleneck of hearing rehabilitation with currently available CIs is their limitation in encoding spectral information resulting from the broad current spread from each electrode contact. Because light can be confined in space more conveniently, optogenetic stimulation of the auditory nerve represents a promising alternative. The development of optogenetic stimulation for future optical CIs requires efforts to de-sign and characterize appropriate optogenetic actuators, viral gene transfer to neurons, and development of multichannel optical CIs. This review article reports the current status of optogenetic stimulation of the auditory pathway and recent breakthroughs in achieving high temporal precision and frequency resolution and in establishing multichannel optical CIs.
From Agribusiness to Agroecology and the Transition towards a Sustainable Food System in Panama

: Agriculture plays a primary role in Panama’s economy. e country boasts a variety of farming operations, with cattle (milk and meat), swine, poultry, aquaculture production systems, in addition to rice, coﬀee, banana, teak and pineapple plantations. Massive land use changes have been occurring in a few decades due also to large expansions of agriculture in Panama. For example, although 25% of the country (1.8 million ha.) is considered suitable for farming, in 2017 it was estimated that 2.9 million ha. , were cultivated, legitimizing this agricultural intensiﬁcation by population growth, which at a rate of 1.4% yearly, has been spurring foods consumption and demand. Agribusiness leads Panama’s agriculture yet, this mode of production has intensiﬁed a use of agrichemicals that have deteriorated land and water resources, causing additional pollution, sedimentation, and soil erosion. Climate change and a lack of land-use regulations that should be protective of biodiversity and family farms constitute grave sources of instability that paradoxically, aﬀect domestic food security. is work reviewed changes that have occurred to Panama’s landscape and its natural resources, in the last three decades. e environmental costs for retaining agricultural competitiveness in the global market continue to deteriorate the country’s ecology. However, attempts towards an establishment of agroecology and other models of sustainable farming are notable, in attempting to avert the impacts caused by an intensiﬁcation of agriculture. Although these eﬀorts remain limited, they demonstrate an increasing interest for agroecology by farmers and an understanding for wanting to achieve a better balance between agriculture, food sovereignty and quality of life. Policies and governmental incentives to assist landowners operating a transition toward agroecology and implications for education reform in the agricultural sciences were considered as well, to highlight the complexity of developing a sustainable food system in Panama.
Digital Commons@Georgia Southern Digital Commons@Georgia Southern

Polycyclic aromatic hydrocarbons (PAHs) with highly toxic compounds mainly exist in small-sized particles and can induce considerable human health risks. Studies on PM 2.5 -bound PAHs and their source-specific human health risks still remain scarce. Daily PM 2.5 samples ( n = 119) were collected every three days from 2016 to 2017 in Taipei city, Taiwan. Fifteen PAHs in PM 2.5 were analyzed via gas chromatography tandem mass spectrometry (GC/MS-MS). We utilized a positive matrix factorization (PMF) model, diagnostic ratios, and potential source contribution function (PSCF) to identify the origins of PM 2.5 -bound PAHs. The annual concentration of total PAHs (TPAH) was 0.79 ± 0.67 ng m – 3 (range = 0.11 – 3.27 ng m – 3 ). The highest and lowest values of TPAH appeared in winter and autumn with a mean of 1.36 ng m – 3 and 0.43 ng m – 3 , respectively. The contributions of high-molecular-weight PAHs (HMW PAHs) to TPAH were notably higher than those of low-molecular-weight PAHs (LMW PAHs) during the sampling period. Benzo[ghi]perylene (BghiP) accounted for the highest percentage (23.9%) of TPAH among selected congeners. Traffic emissions (31.3%) were identified as the predominant contributor to ambient PM 2.5 -bound PAHs, followed by industrial emissions (29.2%), evaporated/unburned oil (22.3%), and biomass/coal combustion (17.1%). Apart from the local sources, PSCF-derived results showed that emissions from industrial activities in northeast China and shipping around the Yellow Sea and East China Sea could affect the PAHs in the study area. Traffic emissions were the strongest contributor to human health risk, thus pointing to the significance of control over vehicle exhaust. This study suggests that it is necessary to distinguish the sources of the PM 2.5 -bound PAHs in order to underpin preventive and mitigative strategies for protecting environmental and public health.
Cellular Signaling beyond the Wiener − Kolmogorov Limit

: Accurate propagation of signals through stochastic biochemical networks involves signi ﬁ cant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress ﬂ uctuations in biomolecular populations. Wiener − Kolmogorov (WK) optimal noise ﬁ lter theory, originally developed for engineering problems, has recently emerged as a valuable tool to estimate the maximum performance achievable in such biological systems for a given metabolic cost. However, WK theory has one assumption that potentially limits its applicability: it relies on a linear, continuum description of the reaction dynamics. Despite this, up to now no explicit test of the theory in nonlinear signaling systems with discrete molecular populations has ever seen performance beyond the WK bound. Here we report the ﬁ rst direct evidence of the bound being broken. To accomplish this, we develop a theoretical framework for multilevel signaling cascades, including the possibility of feedback interactions between input and output. In the absence of feedback, we introduce an analytical approach that allows us to calculate exact moments of the stationary distribution for a nonlinear system. With feedback, we rely on numerical solutions of the system ’ s master equation. The results show WK violations in two common network motifs: a two-level signaling cascade and a negative feedback loop. However, the magnitude of the violation is biologically negligible, particularly in the parameter regime where signaling is most e ﬀ ective. The results demonstrate that while WK theory does not provide strict bounds, its predictions for performance limits are excellent approximations, even for nonlinear systems.
Impact of Foliar Spray by Salicylic Acid on Yield and its Attributes of Intercropped Soybean with Sorghum

A field experiment was conducted at Agricultural Research Station Farm in Shandaweel Island, Sohag Governorate to assess the intercropping for grain sorghum with soybean during 2020 and 2021 seasons under different systems about yield and its attributes of the two crops. The experiment was carried out in a randomized complete block design (RCBD) using strip plot arrangement with three replications. The obtained results showed that foliar spray with salicylic acid and intercropping system enhanced significantly all studied soybean and sorghum traits in this study in both seasons. Thus, the highest mean values of previous measured traits were observed from plants that were sprayed with salicylic acid at the rate of 100 ppm of the two crops in the two successful seasons. In addition, maximum yield and its attributes of sorghum were produced from intercropping system of 2 rows sorghum: 2 rows soybean in both seasons. On the other hand, the maximum soybean yield and its related traits were obtained from intercropping system 1 sorghum: 1 soybean and 2 sorghum: 2 soybean of sorghum and in the first and second years, soybean respectively. The greatest ratios of land equivalent (LER) which wear 1.33 and 1.67 in the two respective seasons) were obtained from 2 sorghum: 2 soybean intercropping system under 100 ppm salicylic acid. The greatest values of monetary advantage index (MAI) (6750.40 and 17000.80 LE in the first and second seasons, respectively) were recorded from intercropping system of 2 sorghum: 2 soybean under100 ppm salicylic acid.
An exploration of recreational activities while travelling relative to psychographic tendencies

ABSTRACT Through focusing on active and adventure tourism, a niche of travel becoming increasingly relevant today, this study investigated the association between the preferred recreation activities of individuals while travelling and their psychographic profile characteristics based on Plog’s [(1972). Why destination areas rise and fall in popularity. Paper presented to the Travel Research Association, Southern California Chapter, Los Angeles] model of allocentricity and psychocentricity. Additionally, we investigated the relevancy of this model using individual’s preferred vacation destinations. The activities most likely for respondents to participate in while on vacation were walking, beach activities (sunbathing, walking, and collecting shells), nature walking, swimming, wildlife/nature viewing, and hiking. Results indicated an association between respondent psychographic scores and their projected activity preferences while on vacation.