Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Human Activity Recognition and Recommendation Models for Maintaining Good Health of Mobile Users

With the continuous improvement of the living standard, people have changed their concept from disease treatment to health management. However, most of the current health management software makes recommendations based on users’ static information, with low updating frequency. The effect of targeted suggestions becomes weak with time, and it is hard for the recommendation effect to be satisfactory. Based on the use of smartphones for recognizing human activities in real-time, firstly, a novel 'CNN+GRU' model is proposed in this paper, utilizing both convolutional neural networks (CNNs) and gated recurrent units (GRUs). 'CNN+GRU' can improve the recognition speed and extract the features in sensor data more accurately by achieving in the conducted experiments an average accuracy of 91.27%, thus outperforming other models compared. Secondly, another model, named SimilRec, is proposed for physical activity recommendation to users based on their health profile, the similarities between their current physical activity sequence, and the historical physical activity sequence of other (similar) users.
Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction

Almonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally. Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Trás-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (R2) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (R2 = 0.68). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics.
Examining Obstacles to Quality Management Systems Implementation in Libyan Companies

This study delves into the challenges confronted by Libyan industrial companies during the implementation of Quality Management Systems (QMS), drawing comparisons with analogous cases in different countries. To achieve the outlined objectives, an extensive review of literature was conducted to comprehend the impediments associated with QMS implementation. Subsequently, a survey questionnaire was administered, collecting data from 32 industrial companies. The analysis was performed using SPSS, revealing that out of the 12 identified obstacles, six exhibited statistically significant differences in mean at the p=0.05 level. This indicates specific weaknesses encountered by Libyan companies, including a lack of tangible improvements from previous efforts, insufficient staff commitment, inadequate government support, incentives, and resources, misalignment with company vision/mission, limited financial resources, and a deficiency in technological facilities
Radiolarian assemblages related to the ocean–ice interaction around the East Antarctic coast

Abstract. The Southern Ocean plays a central role in Earth's climate, ecology, and biogeochemical cycles. Therefore, understanding long-term changes in Southern Ocean water masses in the geologic past is essential for assessing the role of the Southern Ocean in the climate system. Radiolarian fossils are a useful tool to reconstruct the water masses of the Southern Ocean. However, the radiolarian assemblages in the high latitudes of the Southern Ocean (south of the polar front (PF)) are still poorly understood. In this paper, we report the radiolarian assemblages in surface marine sediment and plankton tow samples collected from the high latitudes south of the PF. In the surface sediments, four factors (named F1–F4) of the radiolarian assemblages were identified using Q-mode factor analysis, which are related to different water masses and hydrological conditions. F1 is related to the surface waters south of the southern boundary (SB) of the Antarctic Circumpolar Current (ACC), which are cooled by melting sea ice and ice sheets. F2 is associated with water masses north of the SB. A comparison with the vertical distribution of the radiolarian assemblages in plankton tow samples indicates that characteristic species are associated with the Circumpolar Deep Water (CDW) and surface waters north of the SB. F3 is associated with modified Circumpolar Deep Water (mCDW). The radiolarian assemblage of F4 does not seem specifically related to any of the water mass here analyzed. However, the species in this assemblage are typically dwells within ice shelf and/or sea ice edge environments. Radiolarian assemblages here identified and associated with water masses, and ice edge environments are useful to reconstruct the environment south of the PF in the geologic past.
Hardware Circuits and Systems Design for Post-Quantum Cryptography—A Tutorial Brief

Due to the increasing threats from possible large-scale quantum computers, post-quantum cryptography (PQC) has drawn significant attention from various communities recently. In particular, along with the National Institute of Standards and Technology (NIST) PQC standardization process, more works have gradually switched to the PQC hardware implementations. Following this trend, this tutorial brief, led by a group of experts in the field, aims to deliver a comprehensive tutorial on hardware circuits and systems design for PQC. After introducing primary arithmetic operations and algorithmic features of different PQC, we introduced related PQC hardware circuits and systems design techniques (from component to system levels). Future research and directions are also provided. This tutorial will provide useful information for the TCAS-II community and the broader Circuits and Systems Society.
APOL1-G0 protects podocytes in a mouse model of HIV-associated nephropathy

Background African polymorphisms in the gene for Apolipoprotein L1 (APOL1) confer a survival advantage against lethal trypanosomiasis but also an increased risk for several chronic kidney diseases (CKD) including HIV-associated nephropathy (HIVAN). APOL1 is expressed in renal cells, however, the pathogenic events that lead to renal cell damage and kidney disease are not fully understood. Methods The podocyte function of APOL1-G0 versus APOL1-G2 in the setting of a known disease stressor was assessed using transgenic mouse models. Survival, renal pathology and function, and podocyte density were assessed in an intercross of a mouse model of HIVAN (Tg26) with two mouse models that express either APOL1-G0 or APOL1-G2 in podocytes. Results Mice that expressed HIV genes developed heavy proteinuria and glomerulosclerosis, and had significant losses in podocyte numbers and reductions in podocyte densities. Mice that co-expressed APOL1-G0 and HIV had preserved podocyte numbers and densities, with fewer morphologic manifestations typical of HIVAN pathology. Podocyte losses and pathology in mice co-expressing APOL1-G2 and HIV were not significantly different from mice expressing only HIV. Podocyte hypertrophy, a known compensatory event to stress, was increased in the mice co-expressing HIV and APOL1-G0, but absent in the mice co-expressing HIV and APOL1-G2. Mortality and renal function tests were not significantly different between groups. Conclusions APOL1-G0 expressed in podocytes may have a protective function against podocyte loss or injury when exposed to an environmental stressor. This function appears to be absent with APOL1-G2 expression, suggesting APOL1-G2 is a loss-of-function variant.
The Effect Of Taxes, Tunneling Incentives, Bonus Mechanism, Leverage On Transfer Pricing

This study examines the effect of taxes, tunnelling incentives, bonus mechanisms and leverage on transfer pricing decisions in manufacturing companies listed on the Indonesia Stock Exchange (IDX). The study population included listed manufacturing companies on IDX from 2017 to 2021 and had their financial statements published on the IDX. The sampling technique used was purposive sampling, taking thirteen companies with a study period of five years, thus obtaining a total of sixty-five study samples. The statistics used in this examination are taken from the company's financial statements. This study uses a quantitative design combined with data analysis techniques using classical hypothesis testing, descriptive statistics testing and hypothesis testing. The results show that tax, tunnelling incentive and leverage variables significantly impact the transfer pricing indication. The bonus mechanism variable has no impact on the indication of transfer pricing.
A Preliminary Study on the Impact of Household Air Pollution on Adult Respiratory Health in Urban and Rural Settings of Jaipur, India

Background: This study aimed to investigate the association between household air pollution and chronic respiratory illness (CRI) in Jaipur, India. Materials and Methods: A total of 147 participants over 18 years of age from 45 households were randomly included in the study, and follow-up visits were conducted once every fortnight over a three-month period. Spirometry tests were conducted using a portable hand-held digital spirometer. A self-designed, pretested, semi-structured questionnaire was utilized to collect socio-demographic information from the participants. Statistical tests, such as t-tests were used to analyze the association between household air pollution and CRI. Results: The prevalence of COPD and bronchial asthma was 2% and 4%, respectively. Various risk factors for CRI were identified within households, and only 4 episodes of acute exacerbation of chronic respiratory illness were observed, resulting in no incidence density calculation. The mean values of forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) were significantly lower in females (P < 0.001) Also, the mean values of FVC and FEV1 were significantly lower in rural areas (P < 0.005) than urban areas. Conclusion: Our findings highlight the importance of government and stakeholder action to implement clean air policies and protect respiratory health. Further studies are needed to expand on these findings and delve deeper into understanding the complex interactions between various risk factors and respiratory health in this population.
Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

Android malware recognition is the procedure of mitigating and identifying malicious software (malware) planned to target Android operating systems (OS) that are extremely utilized in smartphones and tablets. As the Android ecosystem endures to produce, therefore is the risk of malware attacks on these devices. Identifying Android malware is vital for keeping user data, privacy, and device integrity. Android malware detection utilizing deep learning (DL) signifies a cutting-edge system for the maintenance of mobile devices. DL approaches namely recurrent neural network (RNN) and convolutional neural network (CNN) are best in automatically removing intricate designs and behaviors in Android app data. By leveraging features such as application programming interface (API) call sequences, code patterns, and permissions, these approaches are efficiently differentiated between benign and malicious apps, even in the face of previous unseen attacks. This study presents an Intelligent Pattern Recognition using an Equilibrium Optimizer with Deep Learning (IPR-EODL) Approach for Android Malware Recognition. The purpose of the IPR-EODL approach is to properly identify and categorize the Android malware in such a way that security can be achieved. In the IPR-EODL technique, the data pre-processing step was applied to convert input data into a compatible setup. In addition, the IPR-EODL technique applies channel attention long short-term memory (CA-LSTM) methodology for the recognition of Android malware. To enhance the solution of the CA-LSTM algorithm, the IPR-EODL system employs the Equilibrium optimization (EO) algorithm for the hyperparameter tuning method. The experimentation evaluation of the IPR-EODL model can be verified on a benchmark Android malware database. The extensive results highlight the significant result of the IPR-EODL approach to the Android malware detection process.
Comparison of Efficacy between Myo-Inositol versus Metformin in Women with Polycystic Ovary Syndrome

Objective: To compare the effectiveness of myo-inositol versus metformin in treating women with polycystic ovary syndrome.Study Design: Cross-sectional study.Place and Duration of Study: The study was carried out at the Department of Gynae Unit II, Dr. Ruth. K.M. Pfau th th Civil Hospital Karachi, Pakistan from 7 February 2019 to 6 August 2019.Methods: All eligible patients who visited the hospital were enrolled. The group-Aincluded females who received myo-inositol (n=35), whereas the group-B included females who received metformin (n=35). Females in the myo-inositol group were administered 1 gram of myo-inositol twice daily, while those in the metformin group were administered 500 mg of metformin twice daily. Regular follow-up visits were scheduled every two months, and the efficacy of the treatment was assessed after six months. Efficacy was evaluated in terms of the normalization of menstrual cycles and the achievement of ovulation.Results: The mean age in the myo-inositol and metformin groups was 23.92±3.70 and 23.68±4.23, respectively. The mean weight in the myoinositol and metformin groups was 59.23±2.07 and 61.79±5.92, respectively. Notably, the efficacy rates for the myo-inositol and metformin groups were 68.57% and 20%, respectively with p-value=0.001. Conclusion: Myo-inositol was found to demonstrate significant superiority over metformin in the treatment of polycystic ovary syndrome.How to cite this: Latif N, Rajar S, Yousuf M, Imtiaz S, Abbas S, Jamali Z. Comparison of Efficacy Between Myo-Inositol versus Metformin in Women with Polycystic Ovary Syndrome. Life and Science. 2024; 5(1): 48-53. doi: http://doi.org/10.37185/LnS.1. 1.486
Pengaruh Keputusan Investasi Dan Keputusan Pendanaan Terhadap Nilai Perusahaan Pada Perusahaan Tambang Yang Terdaftar Di Bursa Efek Indonesia

Penelitian ini bertujuan untuk mengetahui keputusan investasi dan keputusan pendanaan terhadap nilai perusahaan pada perusahaan tambang yang terdaftar di Bursa Efek Indonesia baik secara parsial maupun secara simultan. Metode pengumpulan data yang digunakan dalam penelitan ini yaitu dengan menggunakan riset pustaka. Metode analisis data dalam penelitian ini menggunakan regresi linear berganda. Hasil penelitian ini menunjukka bahwa 1) Keputusan Investasi berpengaruh signifikan terhadap Nilai Perusahaan karena nilai t hitung 3,182 lebih besar dari t tabel 2,051 dan nilai sig 0,004 lebih kecil dari probabilitas 0,05. 2) Keputusan Pendanaan tidak berpengaruh sifnifikan terhadap Nilai Perusahaan karena nilai t hitung 0,441 lebih besar dari t tabel 2,051 dan nilai sig 0,662 lebih kecil dari probabilitas 0,05. 3) Keputusan Investasi (X1) dan Keputusan Pendanaan (X2) berpengaruh secara bersama sama terhadap Nilai Perusahaan (Y) karena nilai F hitung 5,191 lebih besar dari F tabel 4,17 dan nilai sig 0,012 lebih kecil dari probabilitas 0,05.
Cloned genes and genetic regulation of anthocyanin biosynthesis in maize, a comparative review

Anthocyanins are plant-based pigments that are primarily present in berries, grapes, purple yam, purple corn and black rice. The research on fruit corn with a high anthocyanin content is not sufficiently extensive. Considering its crucial role in nutrition and health it is vital to conduct further studies on how anthocyanin accumulates in fruit corn and to explore its potential for edible and medicinal purposes. Anthocyanin biosynthesis plays an important role in maize stems (corn). Several beneficial compounds, particularly cyanidin-3-O-glucoside, perlagonidin-3-O-glucoside, peonidin 3-O-glucoside, and their malonylated derivatives have been identified. C1, C2, Pl1, Pl2, Sh2, ZmCOP1 and ZmHY5 harbored functional alleles that played a role in the biosynthesis of anthocyanins in maize. The Sh2 gene in maize regulates sugar-to-starch conversion, thereby influencing kernel quality and nutritional content. ZmCOP1 and ZmHY5 are key regulatory genes in maize that control light responses and photomorphogenesis. This review concludes the molecular identification of all the genes encoding structural enzymes of the anthocyanin pathway in maize by describing the cloning and characterization of these genes. Our study presents important new understandings of the molecular processes behind the manufacture of anthocyanins in maize, which will contribute to the development of genetically modified variants of the crop with increased color and possible health advantages.
Navigating Educational Crossroads: Unveiling the Cultural Journey of Indonesian Graduate Students in European Scholarship Programs

This research departs from the challenges and adaptation processes undertaken by international students from Indonesia. The research aims to provide a deep understanding of the scholarship alumni in Europe’s academic life during their studies. This research adopts a constructivist paradigm with a qualitative case study approach. The data collection in this research uses purposive sampling techniques with in-depth interviews and the data analysis uses thematic analysis. The subjects are European university alumni from Indonesia who have completed their studies within the past 5 years. There are four themes: study abroad fantasies, a touch of foreign cultures, academic problems and challenges, and academic strategies or solutions. There are also problems and challenges encountered such as academic constraints, stress, and weather. Scholarship alumni in Europe have strategies to adapt, such as traveling while studying, self-management, having a support system, and establishing social interaction with Indonesians. The findings and results can become a foundation document for donors, sponsors, scholarship providers, and the Indonesian Embassy in European countries in creating strategic programs as a provision for students in their studies.
La carte variétale de l'arachide au Sénégal

La carte varietale est un outil elabore par la recherche afin de permettre le choix des varietes a cultiver en fonction de la region ciblee. Cette carte represente une synthese des connaissances, actualisee regulierement en fonction des changements ogroclimatiques, des pratiques paysannes et des resultats de recherche. Au coeur du bassin arachidier, les nouvelles conditions pluviometriques ont conduit les paysans a abandonner les anciennes arachides semi-precoces. Parallelement, les chercheurs de l'Isra et du Cirad ont mis en place un programme d'amelioration varietale de l'arachide pour l'adaptation a la secheresse. Ce programme a permis de developper deux nouvelles varietes, Fleur 1 1 et GC 8-35. La variete Fleur 1 1 est en cours de diffusion dans le centre du bassin arachidier; grace a ses caracteristiques physiologiques, elle est capable d'exterioriser une tres bonne production si la saison est favorable et d'assurer une production minimale en cas de secheresse marquee en cours de cycle. Le variete extra-precoce GC 8-35 (80 jours) est le cultivar le plus precoce jamais vulgarise. Sa zone de predilection se situe au nord du bassin arachidier entre les isohyetes 300 et 400 millimetres
Parameter Inversion Method of Multilayered Media Based on Off-Grid Sparse CMP Model With Refined Orthogonal Matching Pursuit

The common middle point (CMP) ground-penetrating radar (GPR) utilizes the time-delay information under different antenna separations to realize layered inversion. However, parameter inversion of multilayered media is mostly subject to heavy computational complexity and worst estimation error in the determination of refraction positions. To address the problems, an effective parameter inversion method is proposed based on off-grid sparse CMP model with refined orthogonal matching pursuit (OMP) for multilayered parameter inversion. In the proposed method, a sparse CMP signal model with accurate reflected wave propagation modeling is developed based on a refraction approximation method. An off-grid sparse CMP model is further constructed based on second-order Taylor expansion to overcome the deviation from the grid node. Then, a refined OMP algorithm based on compressed sensing (CS) is proposed with an off-grid optimization process to achieve accurate off-grid parameter inversion. Finally, the effectiveness of the proposed method is verified by simulations and experiments.
Neighbouring patterns in Australian suburban housing developments

'Sense of community'and 'neighbouring'are two key concepts that are integral tosocial cohesion. Theyhave been the subject of extensive studies in several disciplines including sociology, psychology and built environment.Sense of community is used to describe a feeling of belonging and shared interests among members of a community. Neighbouring is used to refer to the activities engaged in byand betweenneighbours1. Although the physical qualities of neighbourhoodsand suburbsmay affect the formation of the sense of communityand neighbouring relationships, studies that focus on the contribution of the built environment are limited.According toSilverman (1986), a number of researchers have noted the importance of housing in predicting neighbouringbehaviour,but typically have not developed a full model to account for it2.To address this gap, thisstudy aims to evaluate the physical structure and characteristics ofsuburbs and how they contribute to neighbouring interactionsand sense of community.Three suburban residential streets in the City of Greater Geelong have been selectedfor this study. A short survey wasconducted inthesestreets to evaluate the intensity of interactionsbetween the neighbours that reside here. The intensity of neighbouring interactions and the pattern of relationships was documented through Appleyard's mapping technique3. The built environmentqualities have alsobeen assessed through extensive field studies and observations.This study will compare the physical qualities of the most successful and unsuccessful street in terms of the number and intensity of interactionsto discover how the built environment contributesto a sense of community in suburban developments.