Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Diabetic Foot Ulcers Among patients in Babylon, Iraq

: Background: Diabetes Mellitus is defined as an endocrine progressive disease, characterized by either absence of insulin or lack of the response to the action of insulin by the peripheral tissue. It needs an intensive monitoring and control for level of glucose in the blood or it would progress to dangerous micro-and macro-complications. Aim: assess the prevalence of diabetic foot ulcers among diabetic patients in Babylon, Iraq and its relationship to patient’s sociodemographic and living properties. The variables encompassed in this study consist of education level, gender, age, area of residence, marital status, monthly family income, body mass index (BMI), day of walk-in, smoking habits, presence of hypertension, family history of foot ulcers, and duration of diabetes. Method: the data has been collected from hospitalized patients or those who come to the hospital to reveal their pathological condition by asking the patient directly or indirectly by asking an accompanying relative several questions using a specific questionnaires form. Results: The Diabetic Foot Ulcers (DFUs) was in males more than in females, and nearly half of patients were overweight. There were non-significant differences between hypertensive and non-hypertensive individuals, and family history of diabetic foot ulcer. Meanwhile, there was a substantial difference in the, smoking, and duration of diabetes among study population. Conclusion: Diabetic foot ulcer is a serious complication of diabetic disease and it is more common among diabetic patients who are overweight, smokers, unable to walk, uneducated, poor, urban and those with family history of diabetic foot ulcers so the findings reveal that foot ulcers in diabetic patients become a main health problem, so that better approaches and preventive measures should be done to deal with the epidemic.
Smart Cities and the Idea of Smartness in Urban Development – A Critical Review

The concept of smart cities is becoming another mantra for both developing and developed cities. For instance, Indian government in 2015 announced its objective to build one hundred smart cities all over the country. They clearly stated that they are choosing smart development as the underlying concept for their future growth as a way to foster economic development in smart way to avoid the paths of rapid industrialization and pollution of cities as it took place in Europe and United States. The first of these smart cities, Dholera, is already under construction and it attracts journalists and urban planners from all over the world. The aim of this paper is to critically discuss the theoretical backgrounds and the practices of smart cities and examine the ways the concept is implemented. The paper is based on thorough study of literature and examining the two case studies of Dholera (India) and Songdo (South Korea). Smart city is a contested concept without a unified definition. It stems from the idea of digital and information city promoted using information and communication technologies (ICT) to develop cities. By installation of ICT municipalities obtain large sets of data which are then transformed into effective urban policies. One of the pilot projects of this kind was Rio de Janeiro and building the Center of Operations by IBM Company. City made a great investment into the smart information system before two huge events took place – FIFA World Cup in 2014 and Olympic Games in 2016. The project raised many questions including whether and how it improved the life of its citizens and in what way it made the city smart. The other definition of smart city is the idea of smartness in city development in broader sense. It focuses on smart use of resources, smart and effective management and smart social inclusion. Within this view, the ICTs are one component of the concept, by no means its bread and butter. Technologies can be used in a variety of ways. Problem occurs when smart city is viewed as means to make investments from city budgets by any price, by promoting use of smart technologies as the only way forward, as necessity. Many companies, including technological giants IBM and CISCO already participate on many smart city projects supplying technologies for smart city projects. In this study, we are looking into two case studies, the city of Dholera in India and city of Songdo in South Korea, both pompous large scale projects. Smart City technologies are growing market which is projected to be annually worth 20 billion dollars by 2020 [19], with IBM participating in 2000 projects accounting for 3 billion dollars [19]. There are many concerns about these developments, among them the issue of equity, whose purposes the projects are serving, how these initiatives are developing cities and the general idea of smartness in urban context. The research concludes that the ambiguity of smart city definition allowing multiple interpretations is frequently bent and used to promote the lobbying of strong players in cities and in private sector.
OptMSM: Optimizing

A large-scale industrial recommendation platform typically consists of multiple associated scenarios, requiring a uniﬁed click-through rate (CTR) prediction model to serve them simultaneously. Existing approaches for multi-scenario CTR prediction generally consist of two main modules: i) a scenario-aware learning module that learns a set of multi-functional representations with scenario-shared and scenario-speciﬁc information from input features, and ii) a scenario-speciﬁc prediction module that serves each scenario based on these representations. However, most of these approaches primarily focus on improving the former module and neglect the latter module. This can result in challenges such as increased model parameter size, training diﬃ-culty, and performance bottlenecks for each scenario. To address these issues, we propose a novel framework called OptMSM ( Opt imizing M ulti-S cenario M odeling). First, we introduce a simpliﬁed yet eﬀective scenario-enhanced learning module to alleviate the aforementioned challenges. Speciﬁcally, we partition the input features into scenario-speciﬁc and scenario-shared features, which are mapped to speciﬁc information embedding encodings and a set of shared information embeddings, respectively. By imposing an orthogonality constraint on the shared information embeddings to facilitate the disentanglement of shared information corresponding to each scenario, we combine them with the spe-ciﬁc information embeddings to obtain multi-functional representations. Second, we introduce a scenario-speciﬁc hypernetwork in the scenario-speciﬁc prediction module to capture interactions within each scenario more eﬀectively, thereby alleviating the performance bottlenecks. Finally, we conduct extensive oﬄine experiments and an online A/B test to demonstrate the eﬀectiveness of OptMSM.
Android Malware Classiﬁcation Using Optimized Ensemble Learning Based on Genetic Algorithms

: The continuous increase in Android malware applications (apps) represents a signiﬁcant danger to the privacy and security of users’ information. Therefore, effective and efﬁcient Android malware app-classiﬁcation techniques are needed. This paper presents a method for Android malware classiﬁcation using optimized ensemble learning based on genetic algorithms. The suggested method is divided into two steps. First, a base learner is used to handle various machine learning algorithms, including support vector machine (SVM), logistic regression (LR), gradient boosting (GB), decision tree (DT), and AdaBoost (ADA) classiﬁers. Second, a meta learner RF-GA, utilizing genetic algorithm (GA) to optimize the parameters of a random forest (RF) algorithm, is employed to classify the prediction probabilities from the base learner. The genetic algorithm is used to optimize the parameter settings in the RF algorithm in order to obtain the highest Android malware classiﬁcation accuracy. The effectiveness of the proposed method was examined on a dataset consisting of 5560 Android malware apps and 9476 goodware apps. The experimental results demonstrate that the suggested ensemble-learning strategy for classifying Android malware apps, which is based on an optimized random forest using genetic algorithms, outperformed the other methods and achieved the highest accuracy (94.15%), precision (94.15%), and area under the curve (AUC) (98.10%).
Key-Recovery Attack on a Public-Key Encryption Related to Planted Clique

Hudoba [Hud16] proposed a public key encryption (PKE) scheme and conjectured its security to be based on the Planted Clique problem. In this note, we show that this scheme is not secure. We do so by devising an efficient algorithm for the even neighbor independent set problem proposed by [Hud16]. This leaves open the possibility of building PKE based on Planted Clique.
SigmML: Metric meta-learning for Writer Independent Offline Signature Verification in the Space of SPD Matrices

The handwritten signature has been identified as one of the most popular biometric means of human consent and/or presence for transactions held by any number of physical or legal entities. Automated signature verification (ASV), merge popular scientific branches such as computer vision, pattern recognition and/or data-driven machine learning algorithms. Up to now, several metric learning approaches for designing a writer-independent signature verifier, have been developed within a Euclidean framework by means of having their operations closed with respect to real vector spaces. In this work, we propose, for the first time in the ASV literature, the use of a meta-learning framework in the space of the Symmetric Positive Definite (SPD) manifold as a means to learn a pairwise similarity metric for writer-independent ASV. To begin, pairs of handwritten signatures are converted into a multidimensional distance vector with elements corresponding SPD distances between spatial segments of corresponding covariance pairs. We propose a novel meta-learning approach which explores the structure of the input gradients of the SPD manifold by means of a recurrent model, constrained by the geometry of the SPD manifold. The experimental protocols utilize two popular signature datasets of Western and Asian origin in two blind-intra and blind-inter (or cross-lingual) transfer learning approach. It also provide evidence of the discriminating nature of the proposed framework at least when summarized against other State-of-the-Art models, typically realized under a framework of Euclidean, or vector space, nature.
Gas-releasing potential of biogas digestate

Abstract The aim of the present study was to investigate the amount of CH4, CO2 and NH3 in biogas digestate by laboratory experiments. For these experiments, fresh digestate (FD), solid fractions (SFD) and liquid fractions (LFD) of digestate as well as untreated digestate from the secondary fermenter (SD) were used. Gas release was measured by Fourier transform infrared spectrometry. Based on the total solids content, the highest amount of NH3 was released from LFD followed by SD. SFD and FD released similar amounts of NH3. CH4 was released from all digestates, with the exception of SFD. Stirring of digestates at 90 °C led to CH4 concentrations of 237.6 µg·gTS −1 released from LFD, 40.8 µg·gTS −1 from SD and 71.7 µg·gTS −1 from FD after 10 min. CO2 release from digestate is high compared to release of NH3 and CH4, although degassing was not completed within the experimental period. Digestate degassing experiments conducted while stirring at high temperatures are suitable to determine digestate gas-release potential.
Ethnicity Classification Based on Facial Images using Deep Learning Approach

—Race and ethnicity are terminologies used to describe and categorize humans into groups based on biological and sociological criteria. One of these criteria is the physical appearance such as facial traits which are explicitly represented by a person’s facial structure. The field of computer science has mostly been concerned with the automatic detection of human ethnicity using computer vision-based techniques, where it can be challenging due to the ambiguity and complexity on how an ethnic class can be implicitly inferred from the facial traits in terms of quantitative and conceptual models. The current techniques for ethnicity recognition in the field of computer vision are based on encoded facial feature descriptors or Convolutional Neural Network (CNN) based feature extractors. However, deep learning techniques developed for image-based classification can provide a better end to end solution for ethnicity recognition. This paper is a first attempt to utilize a deep learning-based technique called vision transformer to recognize the ethnicity of a person using real world facial images. The implementation of Multi-Axis Vision Transformer achieves 77.2% classification accuracy for the ethnic groups of Asian, Black, Indian, Latino Hispanic, Middle Eastern, and White.
Fishes of the family Mullidae in the Red Sea, with a key to the species in the Red Sea and the Eastern Mediterranean

Ben-Tuvia, Adam and George Wm. Kissil. 1988. Fishes of the Family Mullidae in the Red Sea, with a Key to the Species in the Red Sea and the Eastern Mediterranean. Ichthyological Bulletin o f the J.L. B. Smith Institute o f Ichthyology No. 52, 16 pages. Thirteen species of Mullidae (goatfishes or red mullets) occur in the Red Sea: Mulloides flavolineatus, M. vani­ colensis, Parupeneus cyclostomus, P. forsskali, P. heptacanthus, P. macronemus, P. rubescens, Upeneus asymmetricus, U. moluccensis, U. subvittatus (previously known only from the western Pacific),0 . sulphureus, U. tragula and U. vittatus. Descriptions, biological observations and black and white photographs are given for each of these 13 species. Keys are provided to the genera of Mullidae, to the species occurring in the Red Sea, and to the two Mediter­ ranean species of Mullus that have been reported from the Suez Canal. The distinction between the genera Pseudupeneus and Parupeneus is discussed.
Earthquake induced soft sediment deformation (seismites): new data from the Early Triassic Guryul Ravine section (Kashmir)

At the classical Guryul ravine section of Kashmir, the Permo-Triassic (P-T) boundary is located about 3 m above the base of the Khunamuh Formation. Brookfield et al. (2013) proposed that the deposits straddling the boundary between the Khunamuh Formation and the underlying, Permian Zewan Formation are Siberian Traps-induced seismites overlain by tsunamites. These deposits have been subject to a divergent re-interpretation by Krystyn et al. (2014), who rejected a Siberian-Traps origin. Here, we report the discovery of highly contorted beds at the top of a 7 m. thick, thin-bedded, light beige nodular lime mudstone, a new lithological unit recorded in the Early Triassic Khunamuh Formation, 120 m above the top of the Zewan Formation. These contorted beds, about 1 m thick, are showing typical earthquake induced soft sediment deformations, similar to the latest Permian in the lower part of the section. This new nodular limestone is of early Spathian age as indicated by our conodont sampling and crops out at the base of a cliff-forming limestone interval named Niti Limestone throughout the Tethys Himalaya area since the 19th century. It is interesting to note that both latest Permian and early Spathian seismites occur at a marked lithological change, i.e. a shift in the depositional settings. The latest Permian seismite occurs on a delta ramp with mixed quartzose sand, silt and shelly carbonate lenses, storm influenced deposits, followed by an abrupt contact with the overlying deeper, thin- bedded and siliceous clay mud turbidite deposits and rare lime mud lenses. The early Spathian one is intercalated at the top of a distal ramp nodular limestone deposits, just at the change to the shallower thick-bedded Niti limestone. The latest Permian seismic activity coincides with a platform drowning during a transgressive phase and the early Spathian one occurred during a platform uplift, also during a transgressive phase. Both may conceivably have been driven by recurrent phases of syn-sedimentary block faulting of the northern Indian passive margin. In this, we agree with the conclusions of Krystyn et al. (2014) that any relation between the local occurrences of seismites-tsunamites and the eruption of the Siberian traps is unlikely. Yet, we must keep in mind that both coincide also with global shifts in the geochemical, sedimentological, paleontological and climate records.
Biosorption of Cu(II) Ions by Water Hyacinth Leaf Powder: Process Performance, Kinetics, and Biosorption Isotherm

The water hyacinth leaf powder (WH) was used to adsorb Cu(II) from wastewater. The WH was modified through sulfuric acid (A-WH) and sodium hydroxide (B-WH) treatments. The biosorption was studied with various initial pH, initial Cu(II) concentrations, and biosorp-tion time. The results show that the biosorption capacities of the biosorbents increase with the initial Cu(II) concentration. The optimum pH for the biosorption was 7.5, 7.0, and 7.5 for the WH, A-WH, and B-WH, respectively. The SEM images of the raw and treated WH revealed that alkali treatment could remove lignin more than the acid treatment, leaving more macropores in the B-WH than in the A-WH. The acid and alkali treatments to the WH leaf increase the biosorption capacity of the WH for Cu (II). The pseudo-second-order kinetic model can represent the dynamic behavior of the biosorption better than the pseudo-first-order model. The Langmuir model is better than the Freundlich model for describing the biosorption isotherm. The maximum biosorption capacities of the biosorbents predicted by the Langmuir model were 14.92 mg g−1, 18.32 mg g−1, and 23.27 mg g−1 for WH, A-WH, and B-WH, respectively.
Valuation of Ecological Benefit of Greenery Open Space of Bogor City Using CITYGreen 5.4 Software

Greenery open space is a fundamental part of urban development and management in sustaining the quality of urban environment and the welfare of urban dwellers. A high rate of population growth and limited land owned causing the growth of physical development in the city is done by converting such green open space, agricultural land, forest and other open space for urban development purposes. This study is intended to analyze total value of greenery open space ecological benefits of Bogor City, and provide possible recommendations in order to increase the capacity of its urban ecosystem. This analytical framework is applied to Bogor City considering its peculiarities of greenery open space existence and architectures. The research was conducted by spatial approach through CITYGreen 5.4 software to determine the ecological benefits of greenery open space, based on the trees canopy cover and non trees canopy cover to predict the economic value. CityGreen is a software tool developed by American Forest that helps people understand the value of trees to the local environment. The result showed that CITYGreen 5.4 software can be used to conduct complex analysis of ecosystem services and create easy to understand reports.The software calculate dollar benefits for the services provided by the trees and other greenery open space in absorbed such harmfull pollutants, carbon storage and sequestration, and reducing storm water volumes as natural flood control. The capacity of ecological benefit can still be improved to provide greater benefits in various ways. This software will be very beneficial for city planners in evaluate site plan, and model development scenario that capture the benefits of trees.
Kesedaran Pelajar Terhadap Risiko Bitcoin

This study aims to investigate the awareness of university students towards the risks of a digital currency which is Bitcoin that has been launched by Nakamoto Satoshi in 2008. This study is a quantitative study and a total 200 students from all 8 faculties in Universiti Kebangsaan Malaysia (UKM). Around 25 students from each faculty have been randomly chosen to answer a survey questionnaire. Findings reveal that not all students knew about the risks of Bitcoin. They are aware of the volatility of Bitcoin price, fraud cases using Bitcoin and possibility of losing Bitcoin upon missing of e-wallet. However, they do not aware of the volatility of Bitcoin value depends on the demand and supply of Bitcoin in the market. In addition, the students knew that the usage of ‘cold wallet’ and anti-virus may help in reducing Bitcoin’s risks although they overlook the benefit of ‘backup files’ in minimising unwarranted invasion of Bitcoin privacy.
Correlation between antibiotic resistance and biofilm formation in Klebsiella spp.

Antibiotic resistance among various microbial species is becoming a global threat to human health. MDR, or multidrug resistance, is an organism’s ability to tolerate the effects of many antimicrobial treatments. Klebsiella spp. is the major bacteria that causes a variety of illnesses, including urinary tract infections. The primary goal of this study is to look into the relationship between antibiotic resistance and Klebsiella spp. biofilm production. This study included 26 clinical isolates of Klebsiella spp. (n=26) collected from a tertiary hospital in Bangladesh. Isolates were obtained from blood, wound swabs, urine, and other sources. Following isolation and identification, ampicillin resistance was found in all MDR Klebsiella spp. isolates, followed by Cefotaxime (84.61%), Polymyxin B (84.61%), Amoxicillin (80.76%), Trimethoprim (69.23%), Doripenem (57.69%), Ciprofloxacin (57.69%), Imipenem (50.0%), Meropenem (38.46%), and Oxytetracycline (34.61%). Out of 26 clinical isolates, about 26.92% (n=7) were positive for the bla-NDM-1 gene. However, 15.8% of Klebsiella spp. (n = 4) isolates tested positive for the blaOXA-1 gene. There were no bla-KPC gene positives. Plasmid profile analysis revealed that 24 of 26 Klebsiella spp. isolates included numerous plasmids ranging in size from less than 2kb to more than 10kb. Biofilm formation found that 31% of samples were extremely positive for biofilm formation, 69% were medium biofilm formation, and (n=4) isolates were positive for biofilm resistance gene out of a total of 26 isolates. This study provides an early report on the widespread presence of carbapenem-resistant Klebsiella spp. , demonstrating the need for intensive surveillance systems and research initiatives in Bangladesh to reflect the influence of multidrug resistance features in clinical isolates and their risks. Bangladesh J Microbiol, Volume 40, Number 1, June 2023, pp 25-32
Bird use of arctic tundra habitats at Canning River Delta, Alaska

Seasonal patterns of abundance of shorebirds and Lapland Longspur were studied at the Canning River delta. Study plots with differing habitat characteristics were examined: upland, mesic, and lowland tun­ dra, and coastal saline flats. Nesting density was greatest in the mesic plot, but the lowland received intense use by late summer tran­ sients; use of the saline habitat was consistently high. Cold weather in July, 1980 probably reduced prey availability. Aquatic habitats, especially polygon troughs, produced a high proportion of the adult in sect biomass. Comparison of energetic requirements of birds with the energetic value of their prey supply suggests that food could have limited reproductive success. Availability of both aquatic and ter­ restrial insects may contribute to high breeding bird density in struc turally diverse habitats. Heavy use of wet/flooded tundra by late summer migrants probably reflects abundance of midge (Diptera: chironomidae) larvae in pond sediments.