Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncovering the Dimensions to Increase Islamic Job Satisfaction in Socially Nuanced Economic Institutions

This study aims to explore the dimensions of Islamic Job Satisfaction that will be useful to increase Islamic job satisfaction in socially nuanced economic institutions. Samples were taken from employees of Islamic Microfinance Institutions (IMFI, LKMS, KSSPS BMT) at convenience. Data collection from 30 respondents using a questionnaire instrument was then analyzed using quantitative descriptive methods. The results showed that Islamic Job Satisfaction achieved a very high score for employees of social economic institutions (KSPPS, BMT) was very high (4,088 or 81.76%). The spiritual dimension of Islamic Job Satisfaction received the highest rating (4,386 or 87.72%), while the economic dimension received the lowest rating (3,908 or 78.16%). Future research suggestions take respondents from other socially nuanced economic institutions (educational institutions, health institutions) or other institutions (economic, governmental, social)
The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type

Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.
Mycoplasmas detection in milk samples ¿are the techniques reliable?

Food microbiology is an important aspect that must be considered during food manufacturing, which is, considering the possible sources of contamination, including operators. Highlighting that the group of mycoplasmas are little studied and therefore their presence in foods is not considered. Different mollicutes, particularly mycoplasmas, have been associated with economic losses in the poultry and dairy industries. The objective of this work was to determine the presence of the mycoplasma genus in milk samples through specific microbiological culture and to complement the diagnosis with the PCR technique. Two hundred milk samples were analyzed in Puebla city-Mexico. The samples obtained were centrifuge and reseeded in triplicate on SP4 agar, incubating and analyzed by stereoscopic microscopy. From each milk sample, DNA extraction was carried out with Zymo Research Quick-DNA™ Mini prep kit and primers AR1 and AR2 were used. The microbiological study showed positivity for mycoplasmas of 17% and 39%, in the liquid and solid phase, respectively. With the PCR technique, 53% positivity for mycoplasmas was obtained, which allows us to consider the presence of factors associated with samples of animal origin and that may be interfering with the sensitivity of the technique. Analyzing the viability of techniques to detect microorganisms, particularly mycoplasma, in foods is a practice that is recommended for the well-being of consumers.
Navigating Crises: Examining the Impact on Students in Four European Countries

A crisis is any challenging situation that requires a judgment or a decision. During their lifespan people are exposed to crises at micro, meso and macro levels. Lately, many research deals with crises at macro level. Situations of crises at a macro level that we have been facing such as pandemics, war, earthquakes, and substantial challenges in everyday situations and life lead to the necessity to explore learning and teaching at higher education institutions in the times of crises. The goal of the research was to determine strategies for dealing with crises on macro level among students in higher education. For this purpose, qualitative research was conducted using the focus group method. Four focus groups were held in which a total of 40 students of different profiles from Belgium, Croatia, Poland, and Romania participated. The results show that the biggest challenge for most participants in crisis situations was the absence of social support. In their answers, participants discussed the challenges of distance education, stating "screen fatigue" as an obstacle. Despite this, participants pointed out that times of crisis enabled them to discover their own strengths, which they can use as coping strategies in potential future crises.
Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada

The rapid development of self-driving vehicles requires integrating a sophisticated sensing system to address the various obstacles posed by road traffic efficiently. While several datasets are available to support object detection in autonomous vehicles, it is crucial to carefully evaluate the suitability of these datasets for different weather conditions across the globe. In response to this requirement, we present a novel dataset named the Canadian Vehicle Datasets (CVD). Subsequently, we present deep learning models that use this dataset. The CVD comprises street-level videos which were recorded by Thales, Canada. These videos were collected with high-quality cameras mounted on a vehicle in the Canadian province of Quebec. The recordings were made during daytime and nighttime, capturing weather conditions such as hazy, snowy, rainy, gloomy, nighttime and sunny days. A total of 10000 images of vehicles and other road assets are extracted from the collected videos. A total of 8388 images were annotated with corresponding generated labels 27766 with their respective 11 different classes. We analyzed the performance of the YOLOv8 model trained using the existing RoboFlow dataset. Then, we compared it with the model trained on the expanded version of RoboFlow using the proposed weather-specific dataset, CVD. Final values of improved accuracy of 73.26 %, 72.84 %, and 73.47 % (Precision/Recall/mAP) were reported upon adding the proposed dataset. Finally, the model trained on this diverse dataset exhibits heightened robustness and proves highly beneficial for both autonomous and conventional vehicle operations, making it applicable not only in Canada but also in other countries with comparable weather conditions.
Combining Block Bootstrap with Exponential Smoothing for Reinforcing Non-Emergency Urban Service Prediction

In major urban cities, government authorities have developed various service-requesting systems to report non-emergency public issues related to urban rare events such as noise, blocked driveways, illegal parking, etc. For certain events, request volumes can surge significantly, and timely response depends on accurate prediction. In this paper, we investigate how long it takes to resolve service requests by the agencies. This paper introduces NERPS, a non-emergency response system designed to forecast service request response time. Leveraging urban data, the model establishes connections between historical and future response times. In time series data, applying boot-strapping on the reminder component for generating synthetic data with original time series before fitting the model has been viewed to be effective. The NERPS integrates Holt-Winters with the Moving Block Bootstrap (MBB+HW) model for forecasting the service requests in the NYC dataset. Proposed model forecasts to generate 100-time series values and final prediction obtained by averaging the forecast set. The optimal block size is estimated via the flat-top lag windows technique. This research extends beyond prior studies by comparing the forecasting performance of proposed statistical methods with MI/DL approaches on complex and nonlinear time series data. We consider SARIMA, ARIMA, FB-Prophet, linear regression and basic LSTM as baseline models for response time forecasting and compare the proposed model with multistep ahead point forecasts. The results show that in most cases, the NERPS achieves low RMSE, MAE and Relative Errors among top complaint types and agencies.
Semi-Supervised Deep Conditional Variational Autoencoder for Soft Sensor Modeling

Variational autoencoder (VAE) as an unsupervised deep generated model has been widely applied to process modeling for industrial processes due to its excellent ability in nonlinear and uncertain feature extraction. However, soft sensor based on VAE model faces three challenges. First, the constructed supervised VAE model makes it difficult to describe the correlation between input and output based on self-network. Second, the output of the VAE may suffer from instability and uncontrollability. In addition, the limited labeled data in industries are the third challenge. To solve the above problems, a semi-supervised deep conditional VAE (SS-DCVAE) is constructed for soft sensor based on a supervised DCVAE (S-DCVAE) and an unsupervised DCVAE (U-DCVAE). The S-DCVAE model is constructed by injecting unlabeled data, the actual labels, and estimated labels as constraint conditions from the preneural network. Based on such a conditional supervised structure, the input–output correlation can be strengthened and the generated data can be controlled toward the aim direction. Furthermore, the U-DCVAE model can be built by making the latent distribution as similar as possible to S-DCVAE, as well as only using unlabeled data with corresponding estimated labels. In this way, the unlabeled data can be fully utilized and online prediction can be achieved. Finally, combining the decoder of S-DCVAE model with the encoder of U-DCVAE, the SS-DCVAE model is constructed with both advantages. The effectiveness and superiority of the SS-DCVAE model are demonstrated by comparing the prediction results of the proposed model with other deep learning methods based on industrial cases.
Developing hydrological model for water quality in Iraq marshes zone using Landsat-TM

The Mesopotamia marshlands constitute the largest wetland ecosystem in the Middle East and Western Eurasia. These wetlands are located at the confluence of the Tigris and Euphrates Rivers in southern Iraq. However, there are series reductions in the wetland zones because of neighbor countries, i.e. Turkey, Syria built dams upstream of Tigris and Euphrates Rivers. In addition, the first Gulf war of the 1980s had damaged majority of the marches resources. In fact,the marshes had been reduced in size to less than 7% since 1973 and had deteriorated in water quality parameters. The study integrates Hydrological Model of RMA-2 with Geographic Information System, and remote sensing techniques to map the water quality in the marshlands south of Iraq. This study shows that RMA-2 shows the two dimensional water flow pattern and water quality quantities in the marshlands. It can be said that the integration between Hydrological Model of RMA-2, Geographic Information System, and remote sensing techniques can be used to monitor water quality in the marshlands south of Iraq.
Enhanced low voltage ride-through control of multilevel flying capacitor inverter based wind generation

This paper introduces a cost-effective control method to enhance the low voltage ride-through (LVRT) capability and smooth the output power of a three-phase multilevel flying capacitor inverter (FCI) in wind turbine-based permanent magnet synchronous generator (PMSG). The proposed approach utilizes the energy storage capability of flying capacitors to mitigate wind power fluctuations and address short-duration outages and deep voltage sags. Additionally, a nonlinear controller based Lyapunov theory is developed to regulate capacitor voltages, improve power factors, and balance DC-link voltage. Numerical simulations are conducted in MATLAB/SimPower systems environment to validate the effectiveness of this comprehensive control strategy across different grid operation scenarios.
Sustainable water management and resource recovery and reuse contracts in agricultural in Burkina-Faso for Ecosystem Services sustainability: Using choice experiments to estimate the farmers’ welfare

This paper describes the results of a choice experiment measuring social benefits for sustainable management practices related to water and resource recovery and reuse solutions in agricultural in Burkina-Faso for ecosystem services preservation. Sustainable management is conceptualized with four illustrative practices that impact water availability, water save, soil restoration, soil fertility improvement and productivity growth: storing water with small water infrastructure in rainy season, complete fresh water with waste water from household, watering crop with drip irrigation and fertilizing with organic matter of sludge from septic tank (human faeces). Data for a choice experiment are collected using a face-to-face survey of farmers practicing off-season production in two region (Dano and Ouagadougou) in Burkina- Faso. Results identify substantial benefits for ecosystem services preservation, the use of small water infrastructure, drip irrigation, waste water and organic matter from human faeces. Results also suggest that the estimated household benefits of all fours sustainable management practices combined are similar in magnitude to the benefits from ecosystem services alone. Based on model results, policy and future research may wish to examine possibilities for subsidizing sustainable management practices in urban-influenced areas as a more cost-effective means of providing benefits similar to those realized through ecosystem services sustainability.
Multi-Objective Optimization Technique Based on QUBO and an Ising Machine

With an increase in the complexity of society, solving multi-objective optimization problems (MOPs) has become crucial. In this study, we introduced a novel method called “quadratic unconstrained binary optimization based on the weighted normal” for solving MOPs using Ising machines, such as quantum annealing and digital annealer (DA), in the field of combinatorial optimization. The proposed method applies the penalty-based boundary intersection method to Ising machines under a setting limited to linear objective functions and maximizes the speed and performance of the DA, which is a quadratic unconstrained binary optimization-specific solver. We demonstrated the effectiveness of the proposed method by solving a real-world problem with a nonconvex shaped Pareto front (component combination problem). The results suggested that the proposed method could handle both convex- and nonconvex-shaped Pareto fronts, expanding the potential applications of Ising machines to solving complex MOPs. This development could significantly enhance decision-making processes, particularly in achieving sustainable development goals.
Histopathological analysis of residual lens cells in capsular opacities after cataract surgery using objective software

Purpose: Remnant lens epithelial cells (LECs) within the capsular bag (CB) undergo epithelial-to-mesenchymal transition (EMT) and acquire a myofibroblast phenotype, depositing extracellular matrix (ECM) components, leading to posterior capsular opacification (PCO). This study histopathologically analyzes the LEC-to-myofibroblast transition and de novo ECM component deposition (i.e., smooth muscle actin (SMA) and fibronectin (FN) expression) and determines the intraocular lens (IOL) and patient factors associated with these changes. Methods: In total, 190 CBs with IOLs were removed from donor eyes. Digital images were obtained, and PCO was graded using published software (ADOS, Medical Parachute). Automated immunohistochemistry was performed using anti-SMA to detect EMT and anti-FN to document ECM remodeling. Slides were digitized and analyzed using the Positive Pixel Count v9 algorithm. Linear regression and Poisson regression were performed (P < 0.05). Results: SMA positive expression decreased as the time of IOL implantation increased (P < 0.0001). Positivity of SMA and FN demonstrated a positive correlation (P = 0.0002). Controlling for confounding factors in Poisson regression, hydrophobic and hydrophilic materials showed higher FN and SMA expression when compared to silicone material lenses (FN; P = 0.018; P < 0.0001, SMA; P = 0.001; P = 0.003, respectively). The square optic design had 29% higher SMA positivity compared to the opti-edge design (P = 0.042). One-piece haptic lenses had higher SMA expression compared to three-piece haptic (P = 0.042). A higher risk of expression of SMA and FN was seen in patients with a history of smoking, hypertension, and glaucoma (P < 0.05). Conclusion: This study demonstrated that SMA and FN expression is different according to IOL design and patient factors, thus indicating that LEC changes depend on lens biocompatibility. Therefore, by analyzing the histopathological composition of PCO by using LECs, further insight into the characteristics of IOLs that are important for biocompatibility can be ascertained.
Testing the Application of the Integral AQAL Model in Entrepreneurial Coaching in South African Business Incubators

Objective: Despite significant investment in entrepreneurial coaching in South Africa, the failure rate of small businesses remains high. This empirical study addresses this issue by creating an effective entrepreneurial coaching framework with the help of business incubators, entrepreneurs, coaches, and industry experts. Research Design & Methods: The study used a quantitative cross-sectional design, with a questionnaire distributed to 296 entrepreneurs and statistical analysis performed using SPSS and STATA software. Findings: The findings support the AQAL model of Ken Wilber in how the entrepreneurial outcomes relate to each other. The findings also align with the literature's emphasis on relationship building as a key factor for business growth, with relationship building emerging as the only statistically significant predictor of business growth. Implications and Recommendations: Relationship building should be prioritised over other coaching outcomes, such as self-efficacy, entrepreneurial resilience, and visionary skills, by entrepreneur development practitioners. Contribution & Value Added: These findings have implications for entrepreneurial coaches, policymakers, and professional bodies, urging them to understand entrepreneurs' contexts, take a systemic approach to entrepreneurial coaching, and provide guidance on industry developments and best practices. It is expected that by implementing these recommendations, the proposed entrepreneurial coaching framework will contribute to better outcomes for entrepreneurs and their incubated businesses.
Secure Data Transmission Design Using 32-Bit Memory Block

As technology advances at an ever-increasing rate, the difficulty of protecting data from hackers rises in the advanced world. By using cutting-edge methods, hackers are attempting to get past security measures. Citizens' worries about data privacy are rising as a result. Data breaches can be prevented by using hardware or software. The FPGA board is adaptable and simple to operate. FPGA also offers effectiveness. The study uses software to implement the Advanced Encryption Standard encryption technique. It uses the AES cryptographic security method. AES is implemented by using the 2014.7 ISE Design Suite from Xilinx. The Spartan-6 FPGA displays the results. On the Spartan-6, we addressed the Advanced Encryption Standard Algorithm's implementation and resource requirements.
Recombinase Polymerase Amplification Combined with Lateral Flow Dipstick Assay for the Rapid and Sensitive Detection of Pseudo-nitzschia multiseries

The harmful algal bloom (HAB) species Pseudo-nitzschia multiseries is widely distributed worldwide and is known to produce the neurotoxin domoic acid, which harms marine wildlife and humans. Early detection and preventative measures are more critical than late management. However, the major challenge related to early detection is the accurate and sensitive detection of microalgae present in low abundance. Therefore, developing a sensitive and specific method that can rapidly detect P. multiseries is critical for expediting the monitoring and prediction of HABs. In this study, a novel assay method, recombinase polymerase amplification combined with lateral flow dipstick (RPA-LFD), is first developed for the detection of P. multiseries. To obtain the best test results, several important factors that affected the amplification effect were optimized. The internal transcribed spacer sequence of the nuclear ribosomal DNA from P. multiseries was selected as the target region. The results showed that the optimal amplification temperature and time for the recombinase polymerase amplification (RPA) of P. multiseries were 37 °C and 15 min. The RPA products could be visualized directly using the lateral flow dipstick after only 3 min. The RPA-LFD assay sensitivity for detection of recombinant plasmid DNA (1.9 × 100 pg/μL) was 100 times more sensitive than that of RPA, and the RPA-LFD assay sensitivity for detection of genomic DNA (2.0 × 102 pg/μL) was 10 times more sensitive than that of RPA. Its feasibility in the detection of environmental samples was also verified. In conclusion, these results indicated that the RPA-LFD detection of P. multiseries that was established in this study has high efficiency, sensitivity, specificity, and practicability. Management measures made based on information gained from early detection methods may be able to prevent certain blooms. The use of a highly sensitive approach for early warning detection of P. multiseries is essential to alleviate the harmful impacts of HABs on the environment, aquaculture, and human health.
Potential of Gut Microbial Metabolites in Treating Osteoporosis and Obesity: A Network Pharmacology and Bioinformatics Approach

Background The gut microbial metabolites demonstrate significant activity against metabolic diseases including osteoporosis (OP) and obesity, but active compounds, targets, and mechanisms have not been fully identified. Hence, the current investigation explored the mechanisms of active metabolites and targets against OP and obesity by using network pharmacology approaches. Material/Methods The gutMGene database was used to collect gut microbial targets-associated metabolites; DisGeNET and OMIM databases were used to identify targets relevant to OP and obesity. A total of 63 and 89 overlapped targets were considered the final OP and obesity targets after creating a Venn diagram of metabolites-related targets and disease-related targets. Furthermore, the top 20% of degrees, betweenness, and closeness were used to form the sub-network of protein–protein interaction of these targets. Finally, the biotransformation-increased receptors and biological mechanisms were identified and validated using ADMET properties analysis, molecular docking, and molecular dynamic simulation. Results GO, KEGG pathway analysis, and protein–protein interactions were performed to establish metabolites and target networks. According to the enrichment analysis, OP and obesity are highly linked to the lipid and atherosclerosis pathways. Moreover, ADMET analysis depicts that the major metabolites have drug-likeliness activity and no or less toxicity. Following that, the molecular docking studies showed that compound K and TP53 target have a remarkable negative affinity (−8.0 kcal/mol) among all metabolites and targets for both diseases. Finally, the conformity of compound K against the targeted protein TP53 was validated by 250 ns MD simulation. Conclusions Therefore, we summarized that compound K can regulate TP53 and could be developed as a therapy option for OP and obesity.