Miracle-dz/UniFAR-FTUs huggingface dataset:
ConTrust: A Novel Context-Dependent Trust Management Model in Social Internet of Things

The global population is around 7.4 billion people. This population density requires connectivity to improve the standard of living by transmitting and receiving variety of services. As a result, numerous forms of communication among objects are required for our everyday living demands, independent of their nature. Furthermore, to create a good relationship, every object that is regarded as associate of another object should have distinct criteria such as scalability, interoperability, and trustworthiness. Many security threats, however, have an impact on the social interaction between objects in a social internet of things (SIoT) context, including illegal admittance and suspicious behavior owing to a lack of verification architecture. Others include attempting to provide a proper viewpoint of a malicious object to earn the trust of other objects. As a result, there is a requirement for an acceptable method to check the behavior of objects such as capability, commitment, reliability, and previous job satisfaction before proceeding with any type of job assignment. This will aid in distinguishing between malicious and trustworthy objects by anticipating their upcoming behavior, allowing better judgments regarding service assignment to be made. This study proposes a context-dependent trust management technique (ConTrust) for choosing and allocating jobs in a SIoT environment. The feature-property match approach, as well as the combination of capability, commitment, and satisfaction, were utilized to increase the efficiency of trust assessment and the resolution of context-dependent difficulties. The proposed trust model considers job characteristics, object capabilities and honesty, and the impact of malicious conduct. The experimental results show that the proposed ConTrust model is viable and capable of ensuring the reliability and efficacy of SIoT service sharing between objects as compared to the benchmark models considered in this work.
On $ S $-principal right ideal rings

Let $ S $ be a multiplicative subset of a ring $ R $. A right ideal $ A $ of $ R $ is referred to as $ S $-principal if there exist an element $ s \in S $ and a principal right ideal $ aR $ of $ R $ such that $ As \subseteq aR \subseteq A $. A ring is referred to as an $ S $-principal right ideal ring ($ S $-PRIR) if every right ideal is $ S $-principal. This paper examines $ S $-PRIRs, which extend the notion of principal right ideal rings. Various examples, including several extensions of $ S $-PRIRs are investigated, and some practical results are proven. A noncommutative $ S $-PRIR that is not a principal right ideal ring is found, and the $ S $-variants of the Eakin-Nagata-Eisenbud theorem and Cohen's theorem for $ S $-PRIRs are proven.
Quality-by-Design Principles Applied to the Establishment of a Pharmaceutical Quality Control Laboratory in a Resource-Limited Setting: The Lab Water

Quality-by-design (QbD) is defined as a systematic approach to design and develop a product/service based on sound science and quality risk management. It is already frequently applied in the pharmaceutical industry mainly in the development of pharmaceutical products and analytical methods but is not well established in the setup of facilities like quality control (QC) laboratory (lab). Therefore, lab QbD (lQbD) concept is introduced considering lab water purification system as an example. The water purification system comprising distillation unit coupled with Nanopure Analytical Ultrapure Water System combined with a 0.2-micron filter was established in Jimma University Laboratory of Drug Quality (JuLaDQ). The consistent capability of the established water purification system was evaluated through routine monitoring of the critical quality parameters (i.e., physicochemical, HPLC-DAD chromatogram total peak area, and resistivity) of freshly prepared lab water for a period of one year. In addition, quality of different grade water (tap water, distilled water (before and/or after cleaning distillation unit), and fresh ultrapure water (18.2 MΩ × cm at 25°C)) used in JuLaDQ was evaluated. The results of routine analysis of water quality revealed that HPLC global peak area at 210 and 254 nm could serve as one of the discriminatory control strategies to evaluate the capability of water purification system to produce the desired quality of lab water; and thus, we proposed a specification limit of 5,000 mAU∗s and 5,500 mAU∗s for global peak area at 254 and 210 nm, respectively, as system suitability parameter.
Integrating Qur’anic Stories Into English Language Teaching: Voices From Indonesia

The Qur’an, which includes Qur’anic stories, has been a primary reading text for Muslim learners. Within the growing concern of contextual learning materials, discussion on how Qur’anic stories may have contributed to the English language teaching (ELT) area is still under research. This interdisciplinary study investigates the voices of English practitioners and experts in Islamic education and Qur’anic studies about the usability of Qur’anic stories in ELT. Their voices were used as the basis for designing a learning material prototype. Seventeen English practitioners from three cities in Indonesia, one expert in the field of Islamic education, and two experts in the field of Qur’anic studies were involved in this developmental research. The findings of the research show that Qur’anic stories deserve a place in ELT when they are equipped with reflective thinking activities, appropriate language level, proper assessment system, self-generated illustration, and proper transliteration of Islamic terms. It is recommended that Qur’anic stories be taught within the approach of teaching literature as a resource stressing the morality aspect of the stories.
Algorithm for Energy Resource Allocation and Sensor-Based Clustering in M2M Communication Systems

Recent years have seen a surge in curiosity in machine-to-machine (M2M) collaborations between academics and industry. Machine-to-machine communication devices (MTCDs) are able to communicate automatically and with minimum human intervention in an M2M communications infrastructure. While MTCDs are anticipated to deliver a range of services, resource allocation and clustering approaches in M2M transceivers face issues and limits due to the diverse quality of service (QoS) needs in various network conditions. A major issue in M2M communication systems is how to distribute and cluster resources. This article presents a clustering technique and collaborative resource allocation for MTCD resource management. The clustering and integrated resource allocation challenge is characteristic as a maximization of energy efficiency problem. As a consequence of the original optimization model’s inability to tackle nonlinear fractional utilizations, we separate the issue into two subproblems: power redistribution and cluster. We begin by obtaining the optimal power distribution plan through an iterative energy efficiency maximization algorithm and then offer a modified K -means technique for clustering. The effectiveness of the proposed approach is shown by the numerical solution.
Exposure-response Relationship and Chemical Characteristics of Atmospheric Odor Pollution from a Cigarette Factory

13 Abstract 14 15 Odor pollution is one of the most complained environmental problems in China. At present, the 16 odor control is mainly aimed at municipal facilities. There is a lack of reports on the atmospheric 17 odor pollution produced by some special industrial emissions. This paper studied the odor impact 18 of a cigarette factory on surrounding residents in order to obtain the exposure-response 19 relationship, which can provide a scientific basis for odor control of cigarette production. Exhaust 20 emission measurement, sample collection and laboratory analysis, as well as community survey 21 were carried out. The air dispersion model Aermod was used to simulate the olfactory odor 22 concentrations in surrounding residential areas. Face-to-face questionnaire was used to collect the 23 information of residential responses to the cigarette odor. The exposure-response relationship 24 between the percentage of highly annoyed people (%) and the 98th percentile of odor 25 concentrations was analyzed using the univariate binomial logistic model. In addition, the typical 26 odor pollutants were found by odor activity value. The results showed that the proportion of 27 highly annoyed people in the residential area was ranged from 5% to 64%, and the acceptable 28 level of exposure odor concentration was 4 OU m -3 at the 98th percentile. The main odor 29 substances of the cigarette factory were oxygenated organic substances including esters, fatty 30 acids and ketones. Based on the chemical composition and the odor character of each 31 the odor wheel of cigarette factory was established for the first with the Aermod model, the time series of in the surrounding residential were obtained. Community questionnaire was surveyed to obtain annoyance in eleven sub-areas around the cigarette factory. A logic model was used to study the expose-response relationship to derive a certain acceptable concentration level. The chemical composition of cigarette odors were further to identify the key odor-causing substances. 50 – 80 in each residential based on the population distribution of the area. Face-to-face survey was mainly carried out with fully trained interviewers. The interviewees were residents over the age of 18 who had lived at least 3 months. relation between modeled odor exposure and odor annoyance from cigarette factory. The 336 exposure-response relationship between the percentage of highly annoyed people (HA%) and the 337 98 percentile of odour concentrations (C 98.0, 1-hour ) was established, and C 98.0, 1-hour = 4 OU m -3 was identified as The typical for cigarette were The malodorous. odour
Variation in compensatory strategies as a function of target constriction degree in post-glossectomy speech

Individuals who have undergone treatment for oral cancer oftentimes exhibit compensatory behavior in consonant production. This pilot study investigates whether compensatory mechanisms utilized in the production of speech sounds with a given target constriction location vary systematically depending on target manner of articulation. The data reveal that compensatory strategies used to produce target alveolar segments vary systematically as a function of target manner of articulation in subtle yet meaningful ways. When target constriction degree at a particular constriction location cannot be preserved, individuals may leverage their ability to finely modulate constriction degree at multiple constriction locations along the vocal tract.
Impact of P-Chloroaniline on Oxidative Stress and Biomacromolecules Damage in the Clam Ruditapes philippinarums: A Simulate Toxicity Test of Spill Incident

As a hazardous chemical, p-chloroaniline (PCA) shows intensive adsorption and accumulation after entering the aquatic ecosystem, which can be enriched in organisms and cause damage. With the objective of achieving an integrated and mechanistic view of the toxic effects of PCA in the marine sentinel organism Ruditapes philippinarum, Manila clams were exposed to different concentration of PCA (0.5, 2 and 5 mg/L) for 15 days. Focusing on the gills, first targeting the toxic and digestive gland, the metabolic detoxification organ, we detected dose- and time-related changes inantioxidase activities and biomacromolecular damages in treated clams. Glutathione S-transferase (GST) activity and glutathione (GSH) contents were significantly induced, and superoxide dismutase (SOD) activity increased at the beginning of exposure and then decreased. The malondialdehyde (MDA) and protein methylation (PC) contents which represent lipid peroxidation and carbonylation of proteins, increased first with exposure time and then decreased in the digestive gland. DNA strand break levels were consistently higher than those in the control group. The digestive gland showed more sensitivity to the stress of PCA than the gills. GST and MDA in the gill and GST, GSH, SOD, DNA strand break level in the digestive gland showed significant correlation with PCA exposure, which indicated that these parameters can be used as sensitive biomarkers to indicate toxic effects from chloraniline leakage.
Association of the Mediterranean Dietary Quality Index with handgrip strength and muscle endurance: A cross‐sectional study

Abstract Mediterranean diet is a healthy eating pattern associated with various health advantages. Different scoring methods of adherence to this diet have been used to investigate the association between the Mediterranean diet and muscle outcomes. The present study aimed to investigate the association of the Mediterranean Dietary Quality Index (Med‐DQI) with handgrip strength (HS) and muscle endurance (ME). The current cross‐sectional study involved 268 Iranian adults aged 18–70 years. Anthropometric measures were evaluated. We used the 168‐item food frequency questionnaire (FFQ) to develop the Med‐DQI score. The Med‐DQI score ranges from 0 to 14 (lower score suggests higher adherence to Mediterranean diet). HS was measured by a digital handgrip dynamometer, and ME was the amount of time to reach a maximum of 50% of grip strength. Multiple regression analysis was used to determine the association of Med‐DQI and its components with HS and ME. Med‐DQI score was not associated with mean handgrip strength (MHS) (p = .34) and mean muscle endurance (MME) (p = .69) in the crude model. The associations remained insignificant after the adjustment of covariates (p = .16, .31, respectively). Among the components of Med‐DQI, cholesterol, meats, fish, and cereals were positively correlated with MHS (p < .001 for all). Meats (p = .02), olive oil (p < .001), cereals (p = .04), fruits and vegetables (p < .001 for all) were also positively correlated with MME. According to our findings, Med‐DQI score is not associated with MHS and MME in a population of Iranian adults. Further longitudinal studies are required to confirm these findings.
Statistical analysis of several factors in predicting student performance

Education has always been a key factor in a country's development. Improving student performance is a common goal of students, parents and teachers. Studying the factors that influence student performance can help students focus on their weak points to improve their final grades more effectively. In this paper, random forest algorithm is used to extract the four most important independent variables, which are second-period grade (G2), first-period grade (G1), number of school absences (absences) and number of past class failures (failures) from a dataset on student performance. Then a multiple linear regression model is then established to study the relationship between the dependent variable final grade (G3) and them. After the evaluation of the model fitting accuracy and residual test, a linear model (y = -1.76483 + 0.97847 X1 + 0.14374 X2 + 0.03759 X3 - 0.25720 X4) is built. It simplifies the model and can predict student performance with great accuracy
Social buffer or avoidance depends on the similarity of stress between queen ants

Abstract The association of unrelated ant queens (pleometrosis) is supposed to improve nest foundation and competitiveness under environmental stress, but its evolutionary maintenance is difficult to explain because only one of the queens survives after nest foundation. My aim was to test the potential effect of queen association as a social buffer, that is, as a mechanism reducing stress and improving fitness due to the benefits of social contact. I analyzed the survival, fecundity, and behavior of isolated and paired Lasius flavus queens exposed and not exposed to stressors (disturbing environmental conditions). I found no difference in survivorship between isolated and paired queens or between stressed and unstressed isolated queens. Groups in which 1 or 2 paired queens were stressed showed higher mortality. Unstressed queens died similarly to their stressed nestmates, suggesting stress transmission. A trend suggested that paired queens produced eggs more quickly, but eggs were produced similarly between isolated and paired queens. Social avoidance was observed in groups with 1 stressed and 1 unstressed queen. However, the groups with 2 stressed queens showed the expected behaviors according to social buffering: lower mobility and more interindividual inspection. My findings suggest the synergistic effect of pleometrosis and stress and the dependence of stress level similarity between nestmates on social buffering or rejection on ant queens.
A high gain antenna utilizing Mu-near-zero metasurface structures for 5G applications

Abstract A high gain antenna with Mu-near-zero metasurface (MNZ-MS) is introduced in this work. The proposed antenna can be utilized for future 5G applications. The patch antenna is designed on the upper face of the square substrate with a ground plane at the bottom face. To improve the antenna gain, a layer of MNZ-MS unit cells is placed above the antenna substrate with 0. 48λ0 air space between the two layers, where λ0 is the wavelength in free space at the resonance frequency of 26.3 GHz. The MNZ-MS layer consists of four double-sided split square resonators. The overall thickness of the proposed antenna is 0. 66λ0. To approve the execution of the proposed antenna, a prototype model is manufactured and tested. The outcomes fulfill a reflection coefficient lower than −10 dB over the frequency spectrum of 26–28 GHz. A gain improvement better than 5 dB is obtained compared to the gain of the reference patch at the center frequency of 26.3 GHz.
YOLO-Based Object Detection for Separate Collection of Recyclables and Capacity Monitoring of Trash Bins

This study describes the development of a smart trash bin that separates and collects recyclables using a webcam and You Only Look Once (YOLO) real-time object detection in Raspberry Pi, to detect and classify these recyclables into their correct categories. The classification result rotates the trash bin lid and reveals the correct trash bin compartment for the user to throw away trash. The performance of the YOLO model was evaluated to measure its accuracy, which was 91% under an optimal computing environment and 75% when deployed in Raspberry Pi. Several Internet of Things hardware, such as ultrasonic sensors for measuring trash bin capacity and GPS for locating trash bin coordinates, are implemented to provide capacity monitoring controlled by Arduino Uno. The capacity and GPS information are uploaded to Firebase Database via theESP8266 Wi-Fi module. To deliver the capacity monitoring feature, the uploaded trash bin capacity information is displayed on the mobile application in the form of a bar level developed in the MIT App Inventor for the user to quickly take action if required. The system proposed in this study is intended to be implemented in a rural area, where it can potentially solve the recyclable waste separation problem.
DanceConv: Dance Motion Generation with Convolutional Networks

Automatically synthesizing dance motion sequences is an increasingly popular research task in the broader field of human motion analysis. Recent approaches have mostly used recurrent neural networks (RNNs), which are known to suffer from prediction error accumulation, usually limiting models to synthesize short choreographies of less than 100 poses. In this paper we present a multimodal convolutional autoencoder that combines 2D skeletal and audio information by employing an attention-based feature fusion mechanism, capable of generating novel dance motion sequences of arbitrary length. We first validate the ability of our system to capture the temporal context of dancing in a unimodal setting, by considering only skeletal features as input. According to 1440 rating answers provided by 24 participants in our initial user-study, we show that the optimal performance was presented by the model that was trained with input sequences of 500 poses. Based on this outcome, we train the proposed multimodal architecture with two different approaches, namely teacher-forcing and self-supervised curriculum learning, to deal with the autoregressive error accumulation phenomenon. In our evaluation campaign, we generate 1800 sequences and compare our method against two state-of-the-art approaches. Through qualitative and quantitative experiments we demonstrate the improvements introduced by the proposed multimodal architecture in terms of realism, motion diversity and multimodality, reducing the Fréchet Inception Distance (FID) metric value by 0.39. Subjective results confirm the effectiveness of our approach to synthesize diverse dance motion sequences, reporting a 6% increase in style consistency preference according to 1800 answers provided by 45 evaluators.
Systematic Review of Using Machine Learning in Imputing Missing Values

Missing data are a universal data quality problem in many domains, leading to misleading analysis and inaccurate decisions. Much research has been done to investigate the different mechanisms of missing data and the proper techniques in handling various data types. In the last decade, machine learning has been utilized to replace conventional methods to address the problem of missing values more efficiently. By studying and analyzing recently proposed methods using machine learning approaches, vital adoptions in accuracy, performance, and time consumed can be highlighted. This study aimed to help data analysts and researchers address the limitations of machine learning imputation methods by conducting a systematic literature review to provide a comprehensive overview of using such methods to impute missing values. Novel proposed machine learning approaches used for data imputation are analyzed and summarized to assist researchers in selecting a proper machine learning method based on several factors and settings. The review was performed on research studies published between 2016 and 2021 on adopting machine learning to impute missing values, focusing on their strengths and limitations. A total of 684 research articles from various scientific databases were analyzed using search engines, and 94 of them were selected as primary studies. Finally, several recommendations were given to guide future researchers in applying machine learning to impute missing values.
Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification

Obstetricians often utilize cardiotocography (CTG) to assess a child's physical health throughout pregnancy because it gives data on the fetal heartbeat and uterine contractions, which helps identify whether the fetus is pathologic or not. Obstetricians have traditionally analyzed CTG data artificially, which takes time and is unreliable. As a result, creating a fetal health classification model is essential, as it may save not only time but also medical resources in the diagnosis process. Machine learning (ML) is currently extensively used in fields such as biology and medicine to address a variety of issues, due to its fast advancement. This research covers the findings and analyses of multiple machine learning models for fetal health classification. The method was developed using the open-access cardiotocography dataset. Although the dataset is modest, it contains some noteworthy values. The data was examined and used in a variety of ML models. For classification, random forest (RF), logistic regression, decision tree (DT), support vector classifier, voting classifier, and K-nearest neighbor were utilized. When the results are compared, it is discovered that the random forest model produces the best results. It achieves 97.51% accuracy, which is better than the previous method reported.