Miracle-dz/UniFAR-FTUs huggingface dataset:
The NEL Family of Bacterial E3 Ubiquitin Ligases

Some pathogenic or symbiotic Gram-negative bacteria can manipulate the ubiquitination system of the eukaryotic host cell using a variety of strategies. Members of the genera Salmonella, Shigella, Sinorhizobium, and Ralstonia, among others, express E3 ubiquitin ligases that belong to the NEL family. These bacteria use type III secretion systems to translocate these proteins into host cells, where they will find their targets. In this review, we first introduce type III secretion systems and the ubiquitination process and consider the various ways bacteria use to alter the ubiquitin ligation machinery. We then focus on the members of the NEL family, their expression, translocation, and subcellular localization in the host cell, and we review what is known about the structure of these proteins, their function in virulence or symbiosis, and their specific targets.
ASSESSMENT OF NATURE-INSPIRED ALGORITHMS FOR TEXT FEATURE SELECTION

This paper provides a comprehensive assessment of basic feature selection (FS) methods that have originated from nature-inspired (NI) meta-heuristics; two well-known filter-based FS methods are also included for comparison. The performances of the considered methods are compared on four balanced high-dimensional and real-world text data sets regarding the accuracy, the number of selected features, and computation time. This study differs from existing studies in terms of the extent of experimental analyses that were performed under different circumstances where the classifier, feature model, and term-weighting scheme were different. The results of the extensive experiments indicated that basic NI algorithms produce slightly different results than filter-based methods for the text FS problem. However, filter-based methods often provide better results by using lower numbers of features and computation times.
Recommender System using Reinforcement Learning: A Survey

: Recommender systems are rapidly becoming an integral part of our daily lives. They play a crucial role in overcoming the overloading problem of information by suggesting and personalizing the recommended items. Collaborative filtering, content-based filtering, and hybrid methods are examples of traditional recommender systems which had been used for straightforward prediction problems. More complex problems can be solved with new methods which are applied to recommender systems, such as reinforcement learning algorithms. Markov decision process and reinforcement learning can take part in solving these problems. Recent developments in applying reinforcement learning methods to recommender systems make it possible to use them in order to solve problems with the massive environment and states. A review of the reinforcement learning recommender system will follow the traditional and reinforcement learning-based methods formulation, their evaluation, challenges, and recommended future work.
State-of-the-Art of Cellulose Nanocrystals and Optimal Method for their Dispersion for Construction-Related Applications

In this paper, we reviewed the existing literature on the fabrication of nanocomposites based on cellulose and cellulose nanocrystals (CNCs), and analyzed their dispersion mechanism with respect to their use in the field of construction. First, the existing literature on CNC-based nanocomposites that exhibit the physical and chemical properties of nanocellulose and CNCs was reviewed. Next, keeping the use of these nanocomposites in the field of construction in mind, we determined the optimal mechanical method for their dispersion as an alternative to the currently used harmful chemical techniques. To this end, we evaluated the dispersibility of colloidal CNCs using two dispersion methods: magnetic stirring (for stirring times of 60 min, 120 min, and 180 min) and high-pressure dispersion (at pressures of 345 × 105 Pa, 1035 × 105 Pa, and 1587 × 105 Pa, and one to three dispersion passes). The optimal dispersion conditions were determined by analyzing the size and zeta potential of the CNC particles. It was found that the difference in the average diameter was reduced by approximately 76% at 1587 × 105 Pa during high-pressure dispersion.
Comparative evaluation of Elsenz and GC tooth mousse in remineralization of enamel – a clinical study

Aim: To evaluate the surface micro hardness of enamel following use of ElsenzTM and GC tooth mousse. Methods: Twenty extracted anterior teeth were included for the study. Teeth were decoronated and mounted mesiodistally. Baseline values of the samples were recorded and teeth were randomly divided into 2 groups. Group A: ElsenzTM; Group B: GC tooth mousse. Samples were demineralized with Mc Innes solution (2 cycles, 5 minutes daily with 24-hour gap each) following which remineralization was done for 2 weeks (2 cycles, 5 minutes daily with 7 days gap) and the samples were subjected to micro hardness testing using Vickers hardness tester. The results obtained were subjected to statistical analysis using one way ANOVA and paired t test. Results: GC tooth mousse and ElsenzTM both showed remineralization of enamel, although when statistically compared, there was no significant difference in the micro hardness values in both the groups. Conclusion: Periodic application of ElsenzTM increases micro hardness of enamel and it can be effectively used as remineralizing agent.
Design of a Setup for Modal Testing of Micro-Cantilevers Immersed in Liquid

To facilitate research on the dynamics of micro-cantilevers immersed in liquid, a modal test setup was designed. Modal testing is a powerful form of dynamic testing whereby the system's natural frequencies, modal masses, modal damping ratios, mode shapes, and weight of each of these modes at a certain frequency are determined. Micro-cantilever resonators are often used for sensing applications, where information is extrapolated from the change in dynamical behavior. Such systems are used in liquid environments to characterize the fluid density and viscosity, to measure the mass and stiffness of particles suspended in this liquid, and to image biological samples using atomic force microscopy. The designed setup employs piezoelectric base actuation to excite the cantilevers, of which the response is measured in vacuum, air, and water. A clean and spurious free response is achieved in a bandwidth ranging from 20 kHz to 500 kHz in vacuum and air, and a bandwidth ranging from 50 kHz to 500 kHz in water.
Soft Robotic Grippers for Crop Handling or Harvesting: A Review

Nowadays, harvesting delicate and high-value fruits, vegetables, and edible fungi requires a large input of manual human labor. The relatively low wages and many health problems the workforce faces make this profession increasingly unpopular. Meanwhile, robotic systems that selectively harvest crops are being developed. Whilst the moving platform, manipulator, and image recognition systems of such robots have been studied the past few decades, research on the gripping end of such robots is only since recently growing. This study analyzes the state-of-the-art of soft grippers for crop handling and harvesting, reporting on their quantitative and qualitative characteristics. Seventy-eight grippers are retrieved from the academic literature and compared with each other in terms of their design and reported performance, more specifically grasping and detachment methods, materials used, type of actuators and sensors employed, and the control of the gripping procedure. In addition, the identified grippers are classified into 13 distinct soft grasping technology categories. Moreover, the retrieved papers are analyzed with respect to their publication date and country of origin to observe trends in the recent growth in the field. Furthermore, a subset of soft grippers is identified that was tested on the task of selectively harvesting crops, where grip and detachment success rates and plant and crop damage are compared.
Markov Logic Based Inference Engine for CDSS

CDSS (Clinical Decision Support System) is typically a diagnostic application and a modern technology that can be employed to provide standardized and quality medical facilities to the medical patients especially when expert doctors are not available at the medical centres. These days the use of the CDSSs is quite common in medical practice at remote areas. A CDSS can be very helpful not only in preventive health care but also in computerized diagnosis. However, a typical problem of CDSS based diagnosis is uncertainty. Typically, an ambiguity can occur when a patient is not able to explain the symptoms of his disease in a better way. The typically used forward chaining mechanisms in rule based decision support systems perform reasoning with uncertain data. ML (Markov Logic) is a new technique that has ability to deal with uncertainty of data by integrating FOL (First-Order-Logic) with probabilistic graphical models. In this paper, we have proposed the architecture of a ML based inference engine for a rule based CDSS and we have also presented an algorithm to use ML based forward chaining mechanism in the proposed inference engine. The results of the experiments show that the proposed inference engine would be intelligent enough to diagnose a patient’s disease even from uncertain or incomplete/partial information.
Building International Capacity for Citizen Scientist Engagement in Mosquito Surveillance and Mitigation: The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper

Simple Summary The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper is a free citizen science data collection tool that can be downloaded onto smartphones. The Mosquito Habitat Mapper encourages individuals to participate in locating and removing mosquito breeding habitats from use. An easy-to-use graphic interface enables users to report and describe mosquito breeding habitats, places with standing water where immature mosquitoes grow and develop. Citizen scientists are asked to determine if they see immature mosquitoes and if they wish to count and identify any mosquito larvae they see. In the last task, the user is asked to dump out or cover the standing water source, eliminating its use as a breeding habitat. In this way, the GLOBE Observer mobile app also supports the actions of individuals protecting their communities from mosquito-borne disease. In addition, all data reported by citizen scientists are publicly available. Scientists are accessing this data for a variety of research uses, including the development of automated techniques to recognize larvae and mosquito breeding sites from digital images. Since 2017, more than 32,000 Mosquito Habitat Mapper observations have been submitted by citizen scientists in 84 countries. Abstract The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper is a no-cost citizen scientist data collection tool compatible with Android and iOS devices. Available in 14 languages and 126 countries, it supports mosquito vector surveillance, mitigation, and education by interested individuals and as part of participatory community surveillance programs. For low-resource communities where mosquito control services are inadequate, the Mosquito Habitat Mapper supports local health action, empowerment, and environmental justice. The tangible benefits to human health supported by the Mosquito Habitat Mapper have encouraged its wide adoption, with more than 32,000 observations submitted from 84 countries. The Mosquito Habitat Mapper surveillance and data collection tool is complemented by an open database, a map visualization interface, data processing and analysis tools, and a supporting education and outreach campaign. The mobile app tool and associated research and education assets can be rapidly deployed in the event of a pandemic or local disease outbreak, contributing to global readiness and resilience in the face of mosquito-borne disease. Here, we describe the app, the Mosquito Habitat Mapper information system, examples of Mosquito Habitat Mapper deployment in scientific research, and the outreach campaign that supports volunteer training and STEM education of students worldwide.
Key success factors of total quality management and employees performance in Iraqi oil industry

In times of the business world, under globalisation and overlapping variables, the concept of total quality management (TQM) as a philosophy to excel organisational performance is growing in significance. Many companies across the globe including oil and gas companies employ TQM, as its effectiveness has been observed to improve business performance in various aspects. The oil industry has a critical impact on the economy of Iraq as it represents the backbone and the major source of income to this country. The main aim of this paper is to developing a conceptual framework to improve the employee performance in Iraqi oil industry through applying key success factors of TQM. Therefore, the researcher conducted an extensive literature review on the TQM studies in order to identify and evaluate the key success factors of TQM in addition to the key measures of employee performance. The results of this paper find that there are nine TQM key factors: top management commitment, customer focus, continuous improvement, process management, training and developing, quality culture, employee empowerment and participation and communication. Moreover, three measures of employee performance have been investigated in this study: employee satisfaction and improving work climate. The main finding of this paper is a proposed conceptual framework which contains key success factors of TQM and their impact on employee performance. The proposed framework validate by interviewing ten decision makers in one of the most significant oil company in Iraq which is Iraqi oil company.
Tire Pressure Monitoring System based on Resonant Frequency of Wheel Speed Spectrum

The tire pressure monitoring system(TPMS) is the module implemented in vehicles detecting whether the tire is under-inflated to avoid puncture and destabilization. This paper presents a scheme for indirect TPMS based on wheel speed sensors and onboard hardware. Firstly, a wheel speed signal processing method is raised, including denoising, rim error filtering, and resampling. Secondly, a resonance frequency identification method based on the wheel speed spectrum is proposed based on the characteristics of tire vibration. Finally, the whole process of the method was deployed on the actual vehicle, and the test results proved the effectiveness of the proposed method.
Discovering How to Attack a System

: We evaluate the performance of a genetic algorithm to discover the best set of rules to implement an intrusion against an ICT network. The rules determine how the attacker selects and sequentializes its actions to implement an intrusion. The fitness of a set of rules is assigned after exploiting it in an intrusion. The evaluation of the distinct sets of rules in the populations the algorithm considers requires multiple intrusions. To avoid the resulting noise on the ICT network, the intrusions target a digital twin of the network. We present a preliminary experimental results that supports the feasibility of the proposed solution.
The Social Problems of Morbidly Obese Patients on a Community Level: A Cross-Sectional Study in Saudi Arabia

Objective To identify the predictors and social problems of obese patients in Saudi Arabia. Methods A cross-sectional study was conducted for the period between July 2016 and January 2017. The study population consisted of obese patients visiting the surgery clinics following the medical examinations required before obesity surgery. Patients were recruited from four main medical centres in Riyadh: King Fahad Medical City, University Medical City-King Khalid University Hospital, King Abdulaziz Medical City in the National Guard, and Prince Sultan Military Medical City. The study questionnaire tool was developed based on extensive literature. The questionnaire tool explored personal and demographic characteristics of obese patients and the social problems that morbidly obese patients suffer from at the community level. Logistic regression analysis was used to identify predictors of those facing social problems. Results A total of 374 patients completed the questionnaires. We found that having a bachelor’s degree, a body mass index (BMI) of 40 kg/m2 and above, and other comorbidities were important predictors that increased the likelihood of having social problems related to obesity on a community level (p<0.05). On the other hand, having a high income (11,000 SR/per month and above) was a protective factor that was associated with lower odds of having social problems related to obesity on a community level (p<0.05). Conclusion Obese patients with low socioeconomic status, having a bachelor’s degree, and those with comorbidities tend to have social problems. Future studies to investigate the predictors or social problems among obese patients on a larger scale are warranted.
NF-κB p65 Attenuates Cardiomyocyte PGC-1α Expression in Hypoxia

Hypoxia exerts broad effects on cardiomyocyte function and viability, ranging from altered metabolism and mitochondrial physiology to apoptotic or necrotic cell death. The transcriptional coactivator peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) is a key regulator of cardiomyocyte metabolism and mitochondrial function and is down-regulated in hypoxia; however, the underlying mechanism is incompletely resolved. Using primary rat cardiomyocytes coupled with electrophoretic mobility shift and luciferase assays, we report that hypoxia impaired mitochondrial energetics and resulted in an increase in nuclear localization of the Nuclear Factor-κB (NF-κB) p65 subunit, and the association of p65 with the PGC-1α proximal promoter. Tumor necrosis factor α (TNFα), an activator of NF-κB signaling, similarly reduced PGC-1α expression and p65 binding to the PGC-1α promoter in a dose-dependent manner, and TNFα-mediated down-regulation of PGC-1α expression could be reversed by the NF-κB inhibitor parthenolide. RNA-seq analysis revealed that cardiomyocytes isolated from p65 knockout mice exhibited alterations in genes associated with chromatin remodeling. Decreased PGC-1α promoter transactivation by p65 could be partially reversed by the histone deacetylase inhibitor trichostatin A. These results implicate NF-κB signaling, and specifically p65, as a potent inhibitor of PGC-1α expression in cardiac myocyte hypoxia.
Surface coating of polyamide reverse osmosis membranes with zwitterionic 3‐(3,4‐dihydroxyphenyl)‐l‐alanine (l‐DOPA) for forward osmosis

To overcome low permeability and fouling problems of membranes used in FO processes, modification is needed to improve the hydrophilicity, permeability and selectivity of membranes. In this work, thin film composite (TFC) commercial polyamide RO membranes (BW30‐LE, SW30‐HR, AG and AC) were functionalized with zwitterionic l‐DOPA. The effect of l‐DOPA on the morphology of membranes was determined via SEM, FT‐IR, AFM and contact angle analysis. The l‐DOPA modified BW30‐LE membrane showed excellent properties with 46° contact angle and 3.8 L/m2hbar water permeability and 0.83 L/m2h salt permeability. Although, l‐DOPA modified BW30‐LE membrane had the highest water flux and hydrophilicity, l‐DOPA modified SW30‐HR membrane showed higher FO flux with 9.38 L/m2h than BW 30 membrane with 3.5 L/m2h at 50 g/L NaCl draw solution. Introducing hydroxyl and carboxyl ionic groups on the membrane surface with l‐DOPA coating enhanced the FO performance and water permeability which provide a new insight in FO applications.
LLGF-Net: Learning Local and Global Feature Fusion for 3D Point Cloud Semantic Segmentation

Three-dimensional (3D) point cloud semantic segmentation is fundamental in complex scene perception. Currently, although various efficient 3D semantic segmentation networks have been proposed, the overall effect has a certain gap to 2D image segmentation. Recently, some transformer-based methods have opened a new stage in computer vision, which also has accelerated the effective development of methods in 3D point cloud segmentation. In this paper, we propose a novel semantic segmentation network named LLGF-Net that can aggregate features from both local and global levels of point clouds, effectively improving the ability to extract feature information from point clouds. Specifically, we adopt the multi-head attention mechanism in the original Transformer model to obtain the local features of point clouds and then use the position-distance information of point clouds in 3D space to obtain the global features. Finally, the local features and global features are fused and embedded into the encoder–decoder network to generate our method. Our extensive experimental results on the 3D point cloud dataset demonstrate the effectiveness and superiority of our method.
Cooperative Beamforming for Reconfigurable Intelligent Surface-Assisted Symbiotic Radios

In this paper, we study a novel reconfigurable intelligent surface (RIS) enabled symbiotic radio system, where a RIS is used to enhance the communication between the primary transmitter (PTx) and the primary receiver (PRx), and concurrently transmit its information (e.g., environmental monitoring information) to the PRx by varying the phase shifts. The objective is to cooperatively optimize the active transmit beamforming at the PTx and passive reflecting beamforming at the RIS to minimize the PTx's transmit power, subject to the signal-to-noise ratio constraints of primary and RIS transmissions. A new optimization problem is formulated where the RIS phase shifts are not only related to the channel state information (CSI), but also related to its message. First, we consider the perfect CSI setup to draw useful insights into the cooperative beamforming design between the PTx and RIS. Then, the worst-case robust beamforming design is carried out under the imperfect CSI setup. In particular, we take into account the imperfect successive interference cancellation at the PRx. Finally, simulation results show the effectiveness of the RIS information transfer and the integration of RIS into a symbiotic radio system can significantly improve the performance.
Relations of Public Transport Use and Car Ownership with Neighbourhood and City-Level Travel Purposes in Kerman, Iran

There are significant deviations in travel mode choice drivers between developed and developing countries. This study investigates the determinants of car ownership and public transit ridership in Iran. Using survey data from 800 respondents, the determinants of travel behaviour of Kerman residents were investigated, based on gender, age, household size, car ownership, frequency of public transport ridership, number of working days per week, number of shopping activities in the neighbourhood per week, number of entertainment activities in the neighbourhood per week, and number of shopping activities in the city. Two multivariate models were estimated using the OLS and WLS methods. Our findings suggested that owning a car tends to increase as age, household size, number of working days and number of shopping days in the city decreases. An increase in the number of entertainment days in the neighbourhood raised the probability of car ownership while shopping in the neighbourhood did not influence car ownership. Public transport use was negatively influenced by gender, increased age and number of working days, but positively influenced by shopping in the city. Our research results have significant implications for transport planning. Firstly, changes in household size may not be a good basis to inform planning as our findings show that in Kerman as household size increases, car ownership decreases, and it does not influence public transport. Secondly, when planning road network connectivity (land use) higher working days are expected to increase both car ownership and public transport use.