Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the tractability of data fusion models for detecting anomalies in IoT-based dataset

In recent years Internet of Things (IoT) devices have made their way into many different industries. Deep learning and machine learning methodologies have been applied to many IoT-related tasks123 such as intrusion detection systems or anomaly detection. The efficiency of IoT systems is often hindered by anomalies in data present within the system, often leading to undesirable behavior or possibly a full system shutdown. Due to this, the detection of these anomalies is of the utmost importance. Over the years, various traditional and neural network-based machine learning models have emerged for anomaly detection and classification of corrupted IoT data. However, many of these models fail to capture important features in the data which can lead to false anomaly detection or none at all. In this paper we investigate the applicability of using data fusion to improve the detection of data anomalies. This method uses many different models, such as VGG16, Inception, Xception, and ResNet, to extract features from the data. These extracted features are then fused together, to see if the use of multiple models is better than relying on a single model. This paper also provides a detailed analysis of the efficacy of this fusion-based classification method compared to simpler classification methods. This work investigates the applicability of various machine learning and deep learning models, for anomaly detection in various IoT datasets45.
Learning Control of Robot Manipulators in Task Space

Two important properties of industrial tasks performed by robot manipulators, namely, periodicity (i.e., repetitive nature) of the task and the need for the task to be performed by the end‐effector, motivated this work. Not being able to utilize the robot manipulator dynamics due to uncertainties complicated the control design. In a seemingly novel departure from the existing works in the literature, the tracking problem is formulated in the task space and the control input torque is aimed to decrease the task space tracking error directly without making use of inverse kinematics at the position level. A repetitive learning controller is designed which “learns” the overall uncertainties in the robot manipulator dynamics. The stability of the closed‐loop system and asymptotic end‐effector tracking of a periodic desired trajectory are guaranteed via Lyapunov based analysis methods. Experiments performed on an in‐house developed robot manipulator are presented to illustrate the performance and viability of the proposed controller.
Diet and physical activity and metabolic disorders in patients with schizophrenia and bipolar affective disorder in the Polish population

Introduction There are numerous reports of a higher prevalence of metabolic disorders in patients with schizophrenia and bipolar disorder (BD), yet its connections to diet and physical activity remain not fully explained. This article aimed to evaluate diet, physical activity and selected biochemical and anthropometric parameters associated with metabolism in patients with schizophrenia and BD and to analyse the relationships between these variables in the subjects. Materials and Methods A total of 126 adults participated in the study: 47 patients with schizophrenia, 54 patients with BD and 25 patients in mental illness remission (reference group). Data were collected on the underlying illness and concomitant illnesses, and the severity of symptoms of the current episode was assessed using the following scales: PANSS, MADRS and YMRS. An assessment of the subjects’ diet (KomPAN questionnaire) and their physical activity (International Physical Activity Questionnaire) was carried out. Anthropometric and blood pressure measurements were taken and BMI and WHR were calculated. Serum concentrations of fasting glucose, TSH, total cholesterol, LDL and HDL fractions, triglycerides and leptin, ghrelin and resistin were determined. For statistical analysis, the significance level was set at 0.05. For multiple comparisons one way ANOVA or Kruskal Wallis were used with post hoc Tukey and Dunn tests, respectively. To determine correlation of variables, Pearson’s linear correlation coefficient or Spearman’s rank correlation coefficient were used. Results A total of 50.8% of the subjects had at least one metabolic disorder—most commonly excessive body weight (66.7%) and abdominal obesity (64.3%). Patients did not differ significantly in terms of physical activity, but they did differ in mean time spent sitting—with this being significantly longer for all groups than in the general population. The subjects differed in diet: patients with BD consumed less unhealthy foods than patients with schizophrenia. The highest correlations between physical activity, diet and variables defining metabolic disorders were found in patients with BD. Only in patients with schizophrenia were there significant correlations between the course of the disease and physical activity. Discussion The results suggest the existence of associations between diet, physical activity, and metabolic disorders in both BD and schizophrenia patients. They also suggest a tendency among those patients to spend long periods of time sitting.
The functional unit of neural circuits and its relations to eventual sentience of artificial intelligence systems

The authors discuss a functional unit of neuronal circuits, crucial in both brain structure and artificial intelligence (AI) systems. These units recognize objects denoted by single words or verbal phrases and are responsible for object perception, memorization of image patterns, and retrieval of those patterns as imagination. Functional neuronal units, activated by the working memory system, contribute to problem-solving. The paper highlights the authors' achievements in developing a theory for these functional units, known as 'the equimerec - units', which combine 'the threshold logic unit' and 'the feedback control loop'. The authors emphasize the importance of this theory, especially in the context of highly advanced language-based AI systems. Additionally, the authors note that the functional units' essence relies on backpropagation connections, causing impulse circulation in closed circuits, ultimately leading to the emergence of an electromagnetic field. This phenomenon explains the long-known existence of the human brain's endogenous electromagnetic field. The authors suggest that a similar field likely arises in AI systems. In light of Joe McFadden's "Conscious Electromagnetic Information Field Theory (cemi)", the authors argue that the potential emergence of self-awareness in AI systems deserves due attention.
Watershed Hydrological Responses to Land Cover Changes at Muger Watershed, Upper Blue Nile River Basin, Ethiopia

Changes in land cover (LC) are the major factors influencing the hydrological processes within a watershed. Understanding the impacts of LC on watershed hydrology is crucial for planning and predicting land resource utilization, water resources, and sustaining hydrological balance. This study assesses the hydrological responses of LC changes in the Muger watershed located in the Upper Blue Nile River Basin (UBNRB) from 1986 to 2020. We used the Soil and Water Assessment Tool (SWAT) hydrological model to investigate the effects of LC on the hydrological process. The simulations were driven by several datasets, such as watershed elevations, mean climatology, hydrology and soil datasets, and LC satellite maps for three time periods (i.e., satellite imagery taken in 1986, 2003, and 2020). We found that the key LC changes that affected hydrological parameters in the Muger watershed are changes in cultivation land, forest land, and settlement. The expansion of cultivation land and shrinkage of forest and shrub lands triggered surface runoff and a reduction in groundwater between 1986 and 2003. Additionally, settlement was identified as the primary factor contributing to increases in evapotranspiration (ET) and surface runoff. The LC changes that occurred between 1986 and 2020 reduced the average annual, wet season, and dry season streamflow. Between 2003 and 2020, surface runoff decreased by 3.71% due to the effect of land landscape restoration interventions. The outcome of the study can assist decision-makers and planners in preparing adaptable strategies under changing LC conditions within a watershed.
FSPDA: A Full Sequence Program Data Allocation Scheme for Boosting 3-D nand Flash Read Performance

Multibit 3-D NAND flash-based solid-state disks (SSDs), offering high storage density, contain multiple types of pages to accommodate multiple bits per physical cell. Full sequence program or FSP can program multiple pages in a word line at a time, thereby improving write throughput. Unfortunately, large-grained FSP operations coarsely aggregate consecutive logical pages on the same word line, which adversely affects the parallelism and latency of read requests. Moreover, FSP smooths the program latencies for different types of pages, whereas the pages still exhibit various read latencies. Multiple read latencies and lower read parallelism noticeably deteriorate the completion efficiency of read requests: SSD performance is degraded. To address this issue, we propose an FSP data allocation scheme called FSPDA that incorporates the physical structure characteristics of multibit 3-D NAND, aiming to bolster the read performance of 3-D NAND Flash-based SSDs. FSPDA embraces two distinctive and vital features. First, according to the distance between logical pages, FSPDA allocates logical pages to specified parallel units and stipulates that consecutive logical pages must be assigned to different planes, thus improving read parallelism and data locality. Second, to further reduce read latency, FSPDA employs cache hits to determine hot and cold data to be placed to low-latency and high-latency pages, respectively. We compare FSPDA with two state-of-the-art schemes—OSPADA and single-operation-multiple-location—in terms of multiplane read (MPR) counts, read response time, and GC counts under eight real-world workloads. The experimental results show that compared with the existing schemes, FSPDA slashes the number of MPR counts, read response time, and the number of GC counts by an average of 34.4%, 28.5%, and 13.6%, respectively.
Task Transfer Learning for Prediction of Transient Nitrogen Oxides, Soot, and Total Hydrocarbon Emissions of a Diesel Engine

According to previous studies on internal combustion engine applications using deep learning, deep learning model should be individually optimized and trained to predict different phenomena. This study introduces task transfer learning to predict transient nitrogen oxides (NO $_{\mathrm {x}}$ ), soot, and total hydrocarbon (THC) emissions, which are the major emissions from diesel engines. Using the concept of task transfer learning, when there is a pretrained model relevant to the target task, the model can be transferred to predict another phenomenon by training only the last two layers with hyperparameters of the pretrained model. This concept omits the need for optimizing and training separate models that can save computational time and cost. The results of task transfer learning were evaluated using Worldwide Harmonized Light Vehicles Test Procedure (WLTP) cycle data, which are representative transient cycles of the internal combustion engine, and all possible transfer cases with NOx, soot, and THC emissions were investigated. The R2 values of pretrained NOx, soot, and THC models were 0.9780, 0.9215, and 0.9390, respectively. The R2 gaps between the pretrained and transferred models were within 0.012, with a value of 0.0015 for the NOx emission, 0.011 for the soot emission, and 0.0115 for the THC emission. The relative mean absolute errors (MAEs) to the maximum emission values were approximately 0.57-0.82% for NOx emissions, 0.69-2.02% for soot emissions, and 1.52-2.42% for THC emissions. These accuracy results were comparable to the accuracy of the emission measurement device, which was better than that of the sensors for practical use in vehicles. The results indicated that task transfer learning was valid for predicting emissions of an internal combustion engine, and it achieved efficient organization of prediction models using a pretrained model.
Tropopause Disappearance During the Antarctic Winter

Tropopause disappearance during the final months of the Antarctic winter, and the large annual range (35°C at 15 km.) of stratosphere temperatures, as revealed by 190 radiosonde ascents during 1940–1 at Littie America III, the West Base of the U. S. Antarctic Service, are not paralleled in the Northern Hemisphere and indicate that the Antarctic atmosphere has little exchange of air with the rest of the world. Other evidences of divorce from the world circulation are the coldness of the Antarctic troposphere compared to the Arctic, lower oxygen content of the Antarctic air than that of the rest of the world, asymmetry in world ozone distribution, and the general low pressure prevailing throughout the Antarctic.
Robust local feature descriptor for multisource remote sensing image registration

Abstract. Distinctive and robust local feature description is crucial for remote sensing application, such as image matching and image retrieval. A descriptor for multisource remote sensing image matching that is robust to significant geometric and illumination differences is presented. In the proposed method, a traditional scale-invariant feature transform algorithm is applied for local feature extraction and a feature descriptor, named robust center-symmetric local-ternary-pattern (CSLTP) based self-similarity descriptor, is constructed for each extracted feature point. The main idea of the proposed descriptor is a rotation invariance description strategy on local correlation surface. Unlike common distribution-based descriptors or geometric-based spatial pooling descriptors, the proposed descriptor uses rotation invariance statistically strategic for CSLTP description on a correlation surface, which is inherently rotation invariant and robust to complex intensity differences. Then, a bilateral matching strategy followed by a reliable outlier removal procedure in the geometric transformation model is implemented for feature matching and mismatch elimination. The proposed method is successfully applied for matching various multisource satellite images and the results demonstrate its robustness and discriminability compared to common local feature descriptors.
The emergence of multi-drug resistant and virulence gene carrying Escherichia coli strains in the dairy environment: a rising threat to the environment, animal, and public health

Escherichia coli is a common inhabitant of the intestinal microbiota and is responsible for udder infection in dairy cattle and gastro-urinary tract infections in humans. We isolated E. coli strains from a dairy farm environment in Xinjiang, China, and investigated their epidemiological characteristics, phenotypic and genotypic resistance to antimicrobials, virulence-associated genes, and phylogenetic relationship. A total of 209 samples were collected from different sources (feces, slurry, water, milk, soil) and cultured on differential and selective agar media (MAC and EMB). The presumptive identification was done by the VITEK2 system and confirmed by 16S rRNA gene amplification by PCR. Antimicrobial susceptibility testing was done by micro-dilution assay, and genomic characterization was done by simple and multiplex polymerase chain reaction (PCR). A total of 338 E. coli strains were identified from 141/209 (67.5%) of the samples. Most of the E. coli strains were resistant to sulfamethoxazole/trimethoprim (62.43%), followed by cefotaxime (44.08%), ampicillin (33.73%), ciprofloxacin (31.36%), tetracycline (28.99%), and a lesser extent to florfenicol (7.99%), gentamicin (4.44%), amikacin (1.77%), and fosfomycin (1.18%). All of the strains were susceptible to meropenem, tigecycline, and colistin sulfate. Among the resistant strains, 44.4% were identified as multi-drug resistant (MDR) showing resistance to at least one antibiotic from ≥3 classes of antibiotics. Eighteen out of 20 antibiotic-resistance genes (ARGs) were detected with sul2 (67.3%), blaTEM (56.3%), gyrA (73.6%), tet(B) (70.4%), aph(3)-I (85.7%), floR (44.4%), and fosA3 (100%, 1/1) being the predominant genes among different classes of antibiotics. Among the virulence-associated genes (VAGs), ompA was the most prevalent (86.69%) followed by ibeB (85.0%), traT (84.91%), ompT (73.96%), fyuA (23.1%), iroN (23.1%), and irp2 gene (21.9%). Most of the E. coli strains were classified under phylogenetic group B1 (75.45%), followed by A (18.34%), C (2.96%), D (1.18%), E (1.18%), and F (0.30%). The present study identified MDR E. coli strains carrying widely distributed ARGs and VAGs from the dairy environment. The findings suggested that the dairy farm environment may serve as a source of mastitis-causing pathogens in animals and horizontal transfer of antibiotic resistance and virulence genes carrying bacterial strains to humans via contaminated milk and meat, surface water and agricultural crops.
Incremental Reliability Assessment of Large-Scale Software via Theoretical Structure Reduction

: Problems of software quality assurance and behavior prediction of large-scale software systems have high importance due to the fact that software systems are getting more prevalent in almost all areas of human activities, and always include an large number of modules. To continuously offer signiﬁcant changes or major improvements over the existing system, software upgrading is inevitable. This involves additional difﬁculty to assess reliability and guarantee the quality assurance of the large-scale system. The existing reliability assessment methods cannot continuously yet effectively assess the software reliability because the program structure of the software is not taken into account to drive the assessment process. Thus, it is highly desired to estimate the software reliability in an incremental way. This paper incorporates theoretical sequentialization and reduction of the program structure into sampling-based software reliability evaluation. Speciﬁcally, we leverage importance sampling to evaluate reliability rates of sequence structures, branch structures and loop structures in the software, as well as transition probabilities among these structures. In addition, we sequentialize program structures to support the aggregation of reliability assessment results corresponding to different structures. Finally, a real-world case study is provided as a practical application of the proposed incremental assessment model.
Synthesis and characterization of ground biochar (GB) reinforced composites for removal of heavy metal from palm oil mill effluent (POME)

Heavy metal contamination ruins the ecosystem and water quality. The adsorption method for heavy metal remediation is preferred because of its low cost and high efficiency. This work created eco-friendly ground biochar (GB) biomass-based derivatives reinforced polylactic acid (PLA) with titanium dioxide (TiO2). The composites improved palm oil mill effluent (POME) conditions, and H2SO4 activation increased pores by 80%. PLA and TiO2 altered GB characteristics, according to FTIR analysis. A significant adhesion interaction showed that GB, PLA, and TiO2 particles were compatible. Ball milling’s shear force increased surface area, according to Brunauer-Emmett-Teller (BET) research. Particle size reduction increased GB porosity. Scanning electron microscopy (SEM) was used to study the porous structure of GB and the synergistic effect of PLA and TiO2 on POME during treatment. The SEM showed several components on the composite surface, demonstrating its efficacy. Atomic absorption spectroscopy (AAS) showed that sample C’s composite, which had the most GB, decreased POME heavy metals by 94.4% manganese (Mn), 88.4% cadmium (Cd), and 94.4% zinc (Zn). The resulting POME met the Malaysian Department of Environment’s POME discharge limit by reducing chemical oxygen demand (COD), total suspended solids (TSS), turbidity, and pH.
Socket Preservation Using Dentin Mixed with Xenograft Materials: A Pilot Study

Background: The use of human dentin matrix could serve as an alternative to autologous, allogenic, and xenogeneic bone grafts due to its osteoinductive characteristics. The limitations of its use is tooth availability and that it is often necessary to mix it with a biomaterial. Aim: The aim of this study was to analyze a mix of two different graft materials with different reabsorption ranges when the dentin graft material was not sufficient for full socket preservation. Methods: Seven socket preservation surgeries were carried out employing a mixed graft material containing 50% dentin and 50% xenograft. After four months of recovery, the implants were positioned. At the time of the prosthesis placement and implant surgery, bone samples were collected. Results: The histologic analysis revealed no inflammatory or infective reaction against the seven biopsies. The histomorphometric graft analysis revealed an amount of New Bone of 29.03 ± 6.57% after 4 months and 34.11 ± 5.02% after 8 months. Conclusions: The two graft materials had a different volume reabsorption rate: 71% after 4 months and 90% after 8 months for dentin, and 6% after 4 months and 26% after 8 months for the xenograft. The space created by the dentin reabsorption increased the quantity of new bone.
Ultrafast single-pixel optical imaging based on multimode interference and compressed sensing (Conference Presentation)

Single-pixel imaging based on structured illumination and compressed sensing has opened a new way to compress massive imaging data volume and significantly reduce the cost of image sensor without sacrificing imaging quality. However, conventional structured illumination methods based on digital micro-mirror device (DMD) or a liquid crystal based spatial light modulator (SLM) fall short in fresh rate, making it a real challenge for high-speed imaging applications, which are however of paramount importance in studying dynamic phenomena in living cells, neural activity, and microfluidics, and capturing important rare events. In this work, we propose and demonstrate a new approach for ultrafast (20 Mfps) structured illumination single-pixel imaging using light beam speckles out of a multimode fiber due to multimode interference. Our experimental results show that the excited high-order modes, and hence the multimode interference, are strongly wavelength-dependent. Update of the random speckle patterns can be easily obtained by sweeping the incident wavelength. Ultrafast wavelength sweeping is achieved by stretching ultrafast optical pulses from a mode-locked laser using chromatic dispersion. Extremely broad bandwidth and small wavelength step guarantee a good number of illumination patterns. By measuring multiple dot products of a sparse image with a set of known speckle based random, the image can be reconstructed using an L1 minimization algorithm. The most significance of this completely new design is that multiple (up to thousands) structured illumination measurements can be carried out within a single pulse period, enabling ultrafast pulse-by-pulse imaging. Moreover, thanks to structured illumination and compressed sensing, the proposed structured illumination single-pixel imaging system offers much higher imaging resolution than existing ultrafast photonic time stretch imaging systems for the same captured data size.