Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Climate Change on Groundwater Resources: An Example from Cross River State, Southeastern Nigeria

This paper presents least square equations for predicting some groundwater physical properties (static water level, total dissolved solids, groundwater temperature) in Cross River State, based on some climatic parameters (rain amount, air temperature). This was to assess the impact of future climate variability on the quantity and quality of groundwater in the study area. The results showed that irrespective of the geologic units and seasons, the static water level (SWL), groundwater temperature (GWT), pH and dissolved oxygen (DO) were in stable state conditions based on low standard deviation (SD) values. Secondly, in all the locations occupied, the amount of rainfall (RA) and air temperature (AT) showed significant correlations with SWL, GWT, pH, electrical conductivity (EC) and total dissolved solids (TDS). Predictive equations using climatic change indicators (RA, AT) and groundwater physical properties (SWL, GWT, TDS) were developed and used as a tool for monitoring the change in groundwater quantity and quality due to climate change. The fluctuations observed in groundwater properties (SWL, GWT) for the study period are attributed to the amount of rainfall and air temperature. The variation in TDS is due more to the influence of seawater and brine intrusion. The effect of geology was more or less insignificant. Hence generally, irrespective of the geology, with known amount of precipitation and air temperature, future changes in SWL, TDS and GWT can be predicted at least for limited management and environmental decisions in the study area.
Scalability Analysis of Underwater Wireless Sensor Network by Benchmark Routing Protocols

Underwater Wireless Sensor Networks (UWSNs) play an important role in many ocean communication systems due to their ability to monitor real-time data in a timely and cost-effective way. In order to ensure reliable communication in an UWSN, efficient routing protocols are essential. This paper focuses on the study of different benchmark routing protocols for UWSN to evaluate the performance in terms of throughput, latency and energy efficiency. The protocols chosen for study include the traditional Medium Access Control (MAC) protocols and the state-of-the-art UWSN algorithms such as Underwater Greedy Forwarding (UGF), Underwater Centric routing Protocol (UCRP), and Tone-based Classified Routing (TCR). Results from extensive simulation show that TCR provides the best performance in terms of throughput, latency and energy efficiency. In addition, the energy efficiency of TCR is higher than that of UGF and UCRP. This study provides a benchmark and a point of reference for further development of UWSN routing protocols.
Improved YOLOv7 Fusion Detection Line for Swimming Pool Drowning Detection

In order to ensure the safety of swimmers, an improved method for preventing drowning in video surveillance is proposed by integrating a region-based timing detection line into the YOLOv7 algorithm. The utilization of the region-based timing detection line enables preliminary assessment of swimmer behaviors, thereby reducing the rate of false drowning detections. To enhance the extraction of swimmer behavior features, the YOLOv7 algorithm incorporates the DCNv3 (Deformable Convolutional Network) module. Additionally, a Wise-IoU dynamic focal boundary box regression loss function based on attention is employed to decrease the rate of missed detections and enhance the overall detection accuracy. Experimental results demonstrate that the region-based timing detection line can accurately detect swimmers. Furthermore, the proposed algorithm exhibits a 3.6% improvement in recall rate and a 2.9% increase in average precision when compared to the YOLOv7 algorithm. Notably, it demonstrates superior performance in detecting behaviors of swimmers who are obstructed or located at a distance.
Intelligent Eczema management and awareness system for Saudi Arabia System Architecture

Eczema disease diagnosis and treatment are frequently challenging procedures. Recent development in artificial intelligence (AI) or digital healthcare technology have improved the self-management of severe chronic diseases. for example, skin conditions, insulin, pressure, etc. Numerous research has validated its effectiveness. Additionally, studies have also shown that greater knowledge about any illness enables people to properly control their chronic health conditions, which in turn lowers the problems brought on by disorders such as diabetes, eczema, and hypertension. In this research, a novel dermatitis illness monitoring and treatment system is presented for Saudi Arabian patients. The technology being shown uses AI in conjunction with smart healthcare technology. Employing digital healthcare technologies and AI, it also hopes to arm eczema patients with numerous features about the condition. Comprehensive testing and evaluation of the method that this research proposes will take place in a randomized scientific experiment in Saudi Arabic.
The Use of Time Series Database in Measurements

This paper explores the use of InfluxDB for data analysis, storage, and visualization in measurement science. It focuses on the role of WiFi technology in real-time monitoring and data collection. Although the authors highlight a possible application of InfluxDB to particulate matter measurements, the database can be used for any need also in the field of electrical drives, machines or power electronics to store, analyze, and display measured data. The paper discusses the capabilities of InfluxDB capabilities in efficiently managing and analyzing time-based data and provides a guide for educators and students to effectively work with InfluxDB for data management.
CNN and SVM-based Model for Effective Watermelon Disease Classification

Watermelon farming is hampered by a variety of illnesses that harm its leaves, resulting in lower quality and yield of crops. We present a unique strategy for the precise identification of watermelon disease of the leaves using a model of deep learning in this study. Our model is made up of four convolutional layers, four max pooling levels, and one fully connected layer. Bacterial Fruits Blotch, Anthracnose, Gummy Rope Blight, Downy Mildew, Alternatives Leaves Spot, Cercospora Leaves Spot, Myrothecium Leaves Spot, and powdery Mildew are the eight watermelon leaf diseases to be predicted. For each disease, we analyse recall, precision, and F1-score to determine the model's performance. The obtained results show potential accuracy and efficacy in illness classification. Precision rates vary with the disease, with Anthracnose getting 76.00%, while values for recall also vary, with Gummy Trunk Blight reaching 75.56%. Overall, the model performs well with F1 scores, especially for Anthracnose (73.17%) and Gummy Trunk Blight (73.12%). Furthermore, the micro-average precision of 70.29% and weighting average precision of 70.26% show the model's capacity to manage imbalanced class distribution. These findings highlight the importance of our deep learning-driven technique in assisting in the early detection and discrimination of watermelon leaves diseases, hence contributing to more effective crop health monitoring and assuring long-term watermelon production.
Agricultural Innovation: Federated Learning and CNNs in Guava Leaf Disease Classification

To solve the pervasive problem of guava leaf diseases, this research article provides a ground-breaking study that uses Federated Learning (FL) in conjunction with Convolutional Neural Networks (CNN). The categorization of six common guava leaf diseases is the main emphasis of the research, which makes use of four different client datasets. Large-scale dataset management and use for illness diagnosis present considerable hurdles in the age of big data. Federated learning, a technique that enables model training across several decentralized clients while maintaining data privacy, is how our approach tackles these problems. The model was individually trained for each client using local data, and the model parameters were then combined to create a global model via federated averaging. For the global models from each client, the precision, recall, F1-Score, support, and accuracy varied from 91.73% to 93.67%, 91.68% to 93.68%, 91.70% to 93.66%, 875.17 to 1051.67, and 0.97 to 0.98, respectively. Additionally, the comparison study was done to comprehend the unique contributions made by each customer to the overall model. Macro-average, which calculates the average performance across all courses, varied from 91.70% to 93.66% for all customers. The Micro-average, the mean performance overall individual instances, fell between 92.06% and 93.99%, while the Weighted-average, which considers class imbalance, ranged between 92.07% and 94.01%. The findings of this research provide significant contributions to science and have practical ramifications for the global and Indian agricultural sectors, resulting in improved disease control and higher crop yields.
Herbicide Atrazine Alters the Microbiota of the Filamentous Green Alga Cladophora sp. Cultured from Thailand

The attached green alga Cladophora known to harbor microbiota that play important roles in ecosystem, is one of the most common freshwater filamentous green algae in rivers globally, including those in the northern part of Thailand. These rivers mostly run through agricultural regions where herbicides are heavily used to improve crop quality and quantity. The extensively-used herbicide atrazine persists in soil sediments through transport by surface runoff to rivers. The effect of such herbicide contamination on Cladophora microbiota in Thailand have not been investigated. To acquire this information, 16S r DNA amplicons were used to compare microbiota of Cladophora sp. cultures treated with a spectrum of atrazine concentrations. The results showed that the Cladophora microbiome included at least 106 possible Operational taxonomic units ( OTU s) representing twelve bacterial phyla which are Acidobacteria, Actinobacteria, Armatimonadetes, Chloroflexi, Cyanobacteria, Deinococcus-Thermus, Epsilonbacteraeota, Nitrospirae, Patescibacteria, Planctomycetes, Proteobacteria, and WPS -2, representing both core and local algal bacteria. The presence of atrazine was also correlated with changes in richness of bacterial taxa suggesting that these algal epibiotic bacteria were differently affected by atrazine treatments.
Regression model development and computational procedures to support estimation of real-time concentrations and loads of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-9

In December 2005, the U.S. Geological Survey (USGS), in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria ( Escherichia coli and total coliform), atrazine, and suspended sediment at two USGS streamflow-gaging stations that represent watersheds contributing to Lake Houston (08068500 Spring Creek near Spring, Tex., and 08070200 East Jacinto Tex.). Data from the discrete water-quality samples collected during 2005–9, in conjunction with continuously monitored real-time data that included streamflow and other physical water-quality properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), were used to develop regression models for the estimation of concentrations of water-quality constituents of substantial source watersheds to Lake Houston. The potential explanatory variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, and time (to account for seasonal variations inherent in some water-quality data). The response variables (the selected constituents) at each site were nitrite plus nitrate nitrogen, total phosphorus, total organic carbon, E. coli , atrazine, and suspended sediment. The explanatory variables provide easily measured quantities to serve as potential surrogate variables to estimate concentrations of the selected constituents through statistical regression. Statistical regression also facilitates accompanying estimates of uncertainty in the form of prediction intervals. Each regression model potentially can be used to estimate concentrations of a given constituent in real time. Among other regression diagnostics, the diagnostics used as indicators of general model reliability and reported herein include the adjusted R-squared, the residual standard error, residual plots, and p-values. Adjusted R-squared values for the Spring Creek models ranged from . 582–. 922 (dimensionless). The residual standard errors ranged from . 073–. 447 (base-10 logarithm). Adjusted R-squared values for the East Fork San Jacinto River models ranged from . 253–. 853 (dimensionless). The residual standard errors ranged from . 076–. 388 (base-10 logarithm). In conjunction with estimated concentrations, constituent loads can be estimated by multiplying the estimated concentration by the corresponding streamflow and by applying the appropriate conversion factor. The regression models presented in this report are site specific, that is, they are specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the general methods that were developed and documented could be applied to most perennial streams for the purpose of estimating real-time water quality data.
Holocene river and slope activity in the Visoko Basin, Bosnia‐Herzegovina – climate and land‐use effects

Alluvial and colluvial deposits were investigated at prehistoric tells close to the Bosna River. The chronology is based on ten radiocarbon ages, five optically stimulated luminescence ages, and on the archaeological record. Fluvial activity of the Bosna River is indicated by overbank fines [ca. 5000 (±1000) BC, 500 BC until today] and a phase of lateral displacement of the river bed with subsequent channel fills ca. 2000–500 BC. Early Holocene fluvial activity was probably triggered by climatic variability but might have promoted the expansion of Neolithic societies into the Visoko Basin. Mid‐ to late Holocene river activity occurred under increased climatic aridity but coincided with the expansion of prehistoric settlements to a wider area of the river basin on hard rocks in the adjacent mountainous areas. The destruction of the thin soil cover in the mountains is discussed as a probable trigger for enduring subsequent river activity. This is also indicated by a change in the geochemical signature of the alluvial sediments. The proposed mechanism might explain the mid to late Holocene river history in other regions. The potential of geochemical analyses and diachronous high‐resolution archaeological records to make inferences regarding sediment sources and geomorphological processes is demonstrated. Copyright © 2013 John Wiley & Sons, Ltd.
Geomorphology of the African Rift System

Sedimentation in African rift valleys depends firstly on their structure, especially the recency of tectonic activity and volcanism, secondly on climatic conditions, particularly rainfall, and thirdly on the relief and drainage, the geomorphology. ‘Low Africa’, the N and W of the continent, mostly below 500 m, is characterized by extensive structural basins with intervening updomings locally capped by volcanics. ‘High Africa’, in the S and E, mostly over 1000 m, consists largely of upland plains, dissected by major rivers in the S, interrupted by fault-troughs towards the N, and with extensive volcanic lavas in Ethiopia and Kenya. The Great Rift Valley System is best developed topographically in the region extending northwards from near the mouth of the Zambezi and Lake Malawi (or Nyasa) via Lake Tanganyika to Lake Mobutu (or Albert) and also across the very large updomings thickly covered by volcanics in southern Kenya and southern Ethiopia. To the NE the Ethiopian Rift opens out into the Afar depression, the Gulf of Aden and the Red Sea (Fig. 1). Whereas the northern part of the system is essentially the outcome of late Cenozoic rift faulting, the relief of the southernmost part is associated with much older dislocations (King 1978a). South-central Africa experienced rift faulting in early Mesozoic times. Karroo strata were let down into troughs where they were preserved when later erosion stripped rocks of this age from the areas left upstanding. When crustal rupturing later in the Jurassic resulted in the eastward departure from Africa of Madagascar/India and of Antarctica/Australia, the proto-Zambezi and
Enhancing the Solar PV Plant Based on Incremental Optimization Algorithm

Photovoltaic systems are impacted by the quantity and temperature of sunshine. Due to the competing nature of solar radiation, PV systems operate inefficiently. A variety of maximum power point tracker (MPPT) approaches are utilized to increase the solar system's influence. Incremental optimization (IO), one of the more established MPPT algorithms, provides great steady-state productivity and tracking accuracy over a broad range of shifting atmospheric conditions. Characterizing solar PV features at various irradiances using Matlab or Simulink. The simulation's findings seemed to agree with the different planned PV module efficiencies. In conclusion, it has been found that this optimization technique enhances the PV system's tracking efficiency and response time, leading to dependable grid operation.
Secular increase of the total vertical column abundance of carbon monoxide above central Europe since 1950

The secular increase of the total vertical column abundance of carbon monoxide has been derived from sets of infrared solar spectra recorded from an altitude of 3.58 km at the Jungfraujoch Station, Switzerland, in 1950–1951 and in 1985–1987. The results are based on equivalent width measurements of the R3 line of the 1-0 vibration-rotation band of 12C16O at 2159.30 cm−1. The set of 1985–1987 observations indicates a strong seasonal cycle in the total column abundance of CO, with a ±25% modulation between minimum values in late summer and the maximum values in late winter. Variability on shorter time scales is also present in both the old and recent data sets. The mean cumulative rate of increase of the total column abundance of CO above the Jungfraujoch is found to be (0.85 ± 0.20)%/yr between 1950–1951 and 1985–1987. The present findings are compared with trends reported in earlier studies.
Examining the Accuracy of DNA Parentage Tests Using Computer Simulations and Known Pedigrees

DNA parentage tests are widely used for identifying family in humans, as well as controlling family representation and inbreeding in animals. But, how accurate are these tests? In this study, we hypothesized that current parentage tests are reliable if the analysis involves only one or a few families. We tested the hypothesis by simulating and performing DNA parentage testing on yellow perch fish Perca flavescens with known pedigrees. We found that 100% of the offspring matched their recorded/true parents when one true family was analyzed. However, only 47.25% of offspring matched their true parents when a group of 20 families were evaluated. Our results suggest that DNA parentage tests are reliable as long as the right methods are used, since these tests involve only one family in most cases, and that the results from parentage analyses of large populations can only be used as a reference.
Free Trade Areas: Rising Tides that Can Lift All Boats

This paper employs the gravity model to compare NAFTA-related countries’ trade performance. The paper proposes a new method for defining FTA-related variables to consider the time-varying features of FTAs impact and the potential impact of FTAs on non-members. It also proposes an identification strategy for country-specific time-varying variables in the presence of multilateral resistances, which leads to more insightful performance comparisons across countries. The empirical findings reveal heterogeneity in trade performance across countries during NAFTA, highlighting the possibility for some non-members to outperform member countries. Therefore, support of trade agreements should not be confined solely to agreements directly involving or benefiting the countries of interest, as opportunities for non-members to benefit also exist.
CNN Modelling Untuk Deteksi Wajah Berbasis Gender Menggunakan Python

Pendeteksian wajah (Face Detection) merupakan Pemanfaatan data Biologis (Biometrics) dengan mengidentifikasi ciri-ciri fisik yang ada pada manusia. Digitalisasi pengenalan gender sebagai sebuah teknologi untuk mengenali gender manusia dengan membedakan wajah wanita dan wajah pria berdasarkan fitur Ekstraksi. Keberadaan sistem ini secara implementatif dapat diaplikasikan untuk sistem pengawasan otomatis dan sistem monitoring atau segmentasi pasar berdasarkan trend demografis serta juga dapat diaplikasikan untuk pembatasan akses suatu ruangan. Penelitian ini menggunakan Convolutional Neural Network (CNN). CNN merupakan salah satu jenis neural network dimana metode ini dapat digunakan pada data image. CNN memiliki kemampuan untuk mengenali objek pada suatu image. Secara keseluruhan dataset yang digunakan memiliki 40 anotasi atribut untuk mendeskripsikan image wanita dan pria. Sistem deteksi wajah ini menggunakan python dan Keras sebagai library Machine Learning open source untuk nerve network,dikembangkan untuk membuat penerapan model pembelajaran yang mendalam. Dengan adanya system ini memberikan analisa keakuratan dalam pendeteksian gender sehingga dapat dikembangkan untuk penelitian yang lebih implementatif. Jumlah gambar harus seimbang untuk mendapatkan kinerja yang baik untuk permodelan, masing-masing model akan memiliki folder pelatihan, validasi dan data uji. Jumlah gambar yang tidak seimbang dapat berpengaruh pada kinerja model CNN. Model yang dibangun menggunakan transfer learning dari InceptionV3 dimana permodelan dapat mengenali gender dengan akurasi sebesar 92,6%.
Preferential Gs protein coupling of the galanin Gal 1 receptor in the µ -opioid-Gal 1 receptor heterotetramer

Recent studies have proposed that heteromers of µ -opioid receptors (MORs) and galanin Gal 1 receptors (Gal 1 Rs) localized in the mesencephalon mediate the dopaminergic effects of opioids. The present study reports converging evidence, using a peptide-interfering approach combined with biophysical and biochemical tech- niques, including total internal reflection fluorescence microscopy, for a predominant homodimeric structure of MOR and Gal 1 R when expressed individually, and for their preference to form functional heterotetramers when co-expressed. Results show that a heteromerization-dependent change in the Gal 1 R homodimeric interface leads to a switch in G-protein coupling from inhibitory Gi to stimulatory Gs proteins. The MOR-Gal 1 R heterotetramer, which is thus bound to Gs via the Gal 1 R homodimer and Gi via the MOR homodimer, provides the framework for a canonical Gs-Gi antagonist interaction at the adenylyl cyclase level. These novel results shed light on the intense debate about the oligomeric quaternary structure of G protein-coupled receptors, their predilection for heteromer formation, and the resulting functional significance. R-Gal 1 homodimer. These computational and experimental results indicate that the ability of Gs to couple to the Gal 1 R-Gal 1 R homodimer is determined by the change in its homomeric interface induced by heteromerization with MOR.