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Abstract This article situates the recent explosion of online and app-based sports betting within longer histories of datafication and financialization in sports business and culture. The author tracks four histories in which media technologies and financial logics dissolved previously stable sporting events, athletes, and results into aggregations of data and derivatives that could be leveraged and wagered on. Photography established feelings of confidence and objectivity in horse-race results during the nineteenth and early twentieth centuries. Screen and computational technologies since the 1970s have enabled Las Vegas casino sportsbooks to overwhelm bettors with data abundance. Television and web-based play moved poker at the turn of the millennium away from surprise, chance, and reading people toward probability, risk, and reading data. Around the same time, fantasy sports and management-based video games mirrored financial markets in transforming athletes and leagues into derivatives, that is, disaggregated units that could be reassembled in new digital permutations. Each story helps position contemporary online sports betting within a longer historical arc in which gambling and speculation moved to the center of global economy and culture.

CONFIRMING HEATING TRENDS OF NEAR-SURFACE OCEAN TEMPERATURES, 1988 TO 2022

The issue of global warming captures the imagination of researchers and media people, as well as government leaders. It is in this light that a study was undertaken to discover the changes in ocean temperature off the coast of Hawaii for the years between 1988-1992 and 2018-2022. Data was collected of the top 100 m of the water column in what is known as the mixed layer, from a long-term study sponsored by the National Science Foundation, 100 km off the north coast of Oahu, Hawaii. The study is ongoing and is tasked to collect temperatures roughly once a month, through summer and winter. The scope is limited to this one location with the statistical belief that this comprehensive sample serves as a representative of the population comprising all oceans. If the oceans are warming, the continents should also scientifically be warming. The temperature data for every monthly reading along the entire column at every two meters is averaged for 1988-1992 and then for 2018-2022. The statistical difference between means is calculated and ascertained for 99.49% confidence. The result clearly shows that there is a statistically significant difference in ocean temperatures in the mixed layer over the past 30 years, thus proving beyond a reasonable doubt that ocean temperature increase is taking place.

Fingerprint based smart door lock system using Arduino and smartphone application

In 2023, crime cases in Indonesia reached 105,133. Cases of theft with aggravation dominate the majority of cases. Everyone is concerned about safety, but doors are typically opened and closed using physical keys. This is vulnerable to being tampered with with fake keys, which can lead to house break-ins and theft. In this research, we propose a fingerprint-based wireless door lock design using Arduino and a smartphone. We offer this solution as a preventive measure to reduce the high rate of theft in homes or other buildings. The devices used are Arduino UNO R3, fingerprint sensor, HC-05 Bluetooth module, buzzer, and door lock solenoid. The results of the fingerprint-based wireless door lock using Arduino and a smartphone can function well, with an average response time of 1.20 seconds. Furthermore, testing the HC-05 Bluetooth when sending signals to a smartphone shows that it can read data accurately with an average response time of 1.54 seconds.

Fingerprint based smart door lock system using Arduino and smartphone application

In 2023, crime cases in Indonesia will reach 105,133. Cases of theft with aggravation dominate the majority of cases. Everyone is concerned about safety, but doors are typically opened and closed using physical keys. This is vulnerable to being tampered with with fake keys, which can lead to house break-ins and theft. In this research, we propose a fingerprint-based wireless door lock design using Arduino and a smartphone. We offer this solution as a preventive measure to reduce the high rate of theft in homes or other buildings. The devices used are Arduino UNO R3, fingerprint sensor, HC-05 Bluetooth module, buzzer, and door lock solenoid. The results of the fingerprint-based wireless door lock using Arduino and a smartphone can function well, with an average response time of 1.20 seconds. Furthermore, testing the HC-05 Bluetooth when sending signals to a smartphone shows that it can read data accurately with an average response time of 1.54 seconds.

Lightweight Meter Pointer Recognition Method Based on Improved YOLOv5.

In substation lightning rod meter reading data taking, the classical object detection model is not suitable for deployment in substation monitoring hardware devices due to its large size, large number of parameters, and slow detection speed, while is difficult to balance detection accuracy and real-time requirements with the existing lightweight object detection model. To address this problem, this paper constructs a lightweight object detection algorithm, YOLOv5-Meter Reading Lighting (YOLOv5-MRL), based on the improved YOLOv5 model's speed while maintaining accuracy. Then, the YOLOv5s are pruned based on the convolutional kernel channel soft pruning algorithm, which greatly reduces the number of parameters in the YOLOv5-MRL model while maintaining a certain accuracy loss. Finally, in order to facilitate the dial reading, the dial external circle fitting method is proposed to calculate the dial reading using the circular angle algorithm. The experimental results on the self-built dataset show that the YOLOv5-MRL object detection model achieves a mean average precision of 96.9%, a detection speed of 5 ms/frame, and a model weight size of 5.5 MB, making it better than other advanced dial reading models.

Water PH Monitoring and Control System in Aquariums Based on Internet of Things

Indonesia is one of the countries that has the richest biodiversity in the world such as the diversity of fish species, so many people use them to keep at home as ornamental fish. However, keeping fish with various activities outside the house can result in uncontrolled fish feeding, when excessive feed is given it can affect the environment of the aquarium where the fish are kept, namely the pH value of the water. Based on this, a tool was created that can monitor the pH of aquarium water which is equipped with a control system. Taking advantage of technological advances in this era, monitoring can be done on smartphones via the WhatsApp application. The microcontroller used ESP 32, is equipped with a Wifi module for internet access, so that it can store cloud-based water pH value reading data on the twilio web. Control will be carried out with the aim of maintaining the pH value of the water in the aquarium, namely if the reading results of the pH value are not at a value of 6-8. When the pH value is too low, a liquid that can increase the pH value will be pumped into the aquarium. The same thing will be done when the pH value is high, liquid to lower the pH value will be pumped into the aquarium.

MapReduce based on serverless platforms

This study intends to investigate the application of the MapReduce (MR) framework based on serverless computing in big data processing. By combining the MapReduce model with serverless computing, efficient data processing is achieved. In this framework, the phases of Map task execution, reduce task execution, etc. are accomplished through stateless serverless functions, and data storage is realized with the help of cloud storage platforms (e.g., OSS). In this paper, the author introduces the basic theory of MR, the basic theory of serverless computing, describes the framework implementation process, and discusses the role of OSS in distributed computing. The outcomes of the trial indicate the average execution time of the framework for a WordCount task on 100 pieces of TOEFL English reading data is 6.81 seconds. The discussion analyzes the frameworks advantages (high elasticity, resource utilization) and disadvantages (cold start latency, unsuitable for long time tasks). Future research directions encompass performance optimization, long-time task processing, state management, etc. In summary, this study provides valuable insights for the practical application of serverless computing in big data processing.

Do we still need Illumina sequencing data? Evaluating Oxford Nanopore Technologies R10.4.1 flow cells and the Rapid v14 library prep kit for Gram negative bacteria whole genome assemblies.

The best whole genome assemblies are currently built from a combination of highly accurate short-read sequencing data and long-read sequencing data that can bridge repetitive and problematic regions. Oxford Nanopore Technologies (ONT) produce long-read sequencing platforms and they are continually improving their technology to obtain higher quality read data that is approaching the quality obtained from short-read platforms such as Illumina. As these innovations continue, we evaluated how much ONT read coverage produced by the Rapid Barcoding Kit v14 (SQK-RBK114) is necessary to generate high-quality hybrid and long-read-only genome assemblies for a panel of carbapenemase-producing Enterobacterales bacterial isolates. We found that 30× long-read coverage is sufficient if Illumina data are available, and that more (at least 100× long-read coverage is recommended for long-read-only assemblies. Illumina polishing is still improving single nucleotide variants (SNVs) and INDELs in long-read-only assemblies. We also examined if antimicrobial resistance genes could be accurately identified in long-read-only data, and found that Flye assemblies regardless of ONT coverage detected>96% of resistance genes at 100% identity and length. Overall, the Rapid Barcoding Kit v14 and long-read-only assemblies can be an optimal sequencing strategy (i.e., plasmid characterization and AMR detection) but finer-scale analyses (i.e., SNV) still benefit from short-read data.

Analisis diksi dan gaya bahasa dalam lagu Sewu Kutha karya Didi Kempot: kajian stilistika

This study discusses the use of diction, language style, imagery and figure of speech in the lyrics of the campursari song entitled Sewu Kutha by Didi Kempot. The theory used is a stylistic study. This study aims to: (1) describe the diction (2) describe the imagery (3) describe the figure of speech or language style contained in the lyrics of the campursari song entitled Sewu Kutha by Didi Kempot. The method used in this research is descriptive analysis. The data collection technique used is reading and note-taking technique and data analysis technique using descriptive analysis technique which includes description, classification and analysis. The results of this study are in the lyrics of the campursari song entitled Sewu Kutha created by Didi Kempot using denotative diction, connotative diction, tactile imagery, visual imagery, auditory imagery, comparative language style, affirmation style.

Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory

Holographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This study presents a diffractive DNN (D2NN)-based classifier for holographic memory. D2NNs have so far attracted a great deal of attention for object identification and image transformation at the speed of light. A D2NN, consisting of trainable diffractive layers and devoid of electronic devices, facilitates high-speed data readout. Furthermore, we numerically investigated the classification performance of a D2NN-based classifier. The classification accuracy of the D2NN was 99.7% on 4-bit symbols, exceeding that of the hard decision method.

A Full Replicas-Based Data Store Scheme Inspired by Targeted Immunization of the Epidemic Theory in Smart Manufacturing

Cloud data centers (CDCs) have been used as the basic platforms for data storage in industrial scenarios such as smart manufacturing. However, a lack of effective data storage schemes exacerbates reading latency and replicas' inconsistency in the machine tools of smart facto- ries. In this study, we propose a full-replicas scheme (FRS) to attain the low latency reading and high data consistency required in smart manufacturing. First, inspired by the susceptible-infectious-recovered (SIR) epidemic model, the network bandwidth usage generated by FRS is adjusted by the targeted immunization principle. Then, the final break- out rate is derived as a function of the immunization rate, which can reduce the complexity of target immunization implementation in scale-free networks. Finally, the experi- mental results confirm our theoretical analysis and show that the FRS provides strong consistency with lower client- side reading latency.

Use of a Candida albicans SC5314 PacBio HiFi reads dataset to close gaps in the reference genome assembly, reveal a subtelomeric gene family, and produce accurate phased allelic sequences.

Candida albicans SC5314 is the most-often used strain for molecular manipulation of the species. The SC5314 reference genome sequence is the result of considerable effort from many scientists and has advanced research into fungal biology and pathogenesis. Although the resource is highly developed and presented in a phased diploid format, the sequence includes gaps and does not extend to the telomeres on its eight chromosome pairs. Accurate SC5314 genome assembly is complicated by the presence of extensive repeated sequences and considerable allelic length variation at some loci. Advances in genome sequencing technology provide the tools to obtain highly accurate long-read data that span even the most-difficult-to-assemble genome regions. Here, we describe derivation of a PacBio HiFi data set and creation of a collapsed haploid telomere-to-telomere assembly of the SC5314 genome (ASM3268872v1) that revealed previously unknown features of the strain. ASM3268872v1 subtelomeric distances were up to 19 kb larger than in the reference genome and revealed a family of highly conserved DNA helicase-encoding genes at 10 of the 16 chromosome ends. We also describe alignments of individual HiFi reads to deduce accurate diploid sequences for the most notoriously difficult-to-assemble C. albicans genes: the agglutinin-like sequence (ALS) gene family. We provide a tutorial that demonstrates how the HiFi reads can be visualized to explore any region of interest. Availability of the HiFi reads data set and the ASM3268872v1 comparative guide assembly will streamline research efforts because accurate diploid sequences can be derived using simple in silico methods rather than time-consuming laboratory-bench approaches.

Complete Genome Sequence of a Copper-Resistant Xanthomonas campestris pv. campestris Strain Isolated from Broccoli in Mauritius Suggests Adaptive Gene Gain Through Horizontal Gene Transfer.

Bacterial adaptation is facilitated by the presence of mobile genetic elements and horizontal gene transfer of genes, such as those coding for virulence factors or resistance to antimicrobial compounds. A hybrid assembly of Nanopore MinIon long-read and Illumina short-read data was produced from a copper-resistant Xanthomonas campestris pv.campestris strain isolated from symptomatic broccoli leaves in Mauritius. We obtained a 5.2-Mb high-quality chromosome and no plasmid. We found four genomic islands, three of which were characterized as integrative conjugative elements or integrative mobilizable elements. These genomic islands carried type III effectors and the copper resistance copLABMGF system involved in pathogenicity and environmental adaptation, respectively.

Public Street Lighting Monitoring System Uses Telegram-Based Application Wireless Sensor Network

Public Street Lighting (PJU) usually operates all night until early morning without a remote device to adjust the conditions. In monitoring the PJU lights, the officers are still carrying out their duties manually by visiting the place. This study aims to build a PJU monitoring system tool by utilizing the NodeMCU ESP32 as a microcontroller and a wifi module, which will later send notifications via the Telegram Bot. This system uses the ACS712 current sensor to detect the value of the current flowing in public street lighting installations, the VDC voltage sensor to detect the voltage value, and the LDR sensor to detect light. The read data will be processed by ESP32, which will then be sent to the Telegram Bot. The result of this study is a PJU monitoring tool. With this tool, officers can remotely monitor the PJU lights' condition. The Telegram bot will send notification messages to officers at 07.00 and 19.00. In addition, officers can ask about the state of the lights by instructing "cek pju" to the Telegram Bot. The system has been successfully implemented and operates normally and optimally, demonstrating its efficacy in practical testing

Utilization of Spiking Neural Network (SNN) in X-Ray Image for Lung Disease Detection

The large number of cases of lung disease means that doctors have difficulty in making initial diagnoses, making them prone to misdiagnoses. One type of lung disease that is included in the vulnerable category is pneumonia. Early detection of the condition of the lungs affected by bacterial pneumonia can be carried out by screening using the X-Ray examination modality, namely Digital Radiography (DR). However, in practice, the diagnosis process on Citra DR takes a long time because it requires competent medical personnel (specialists). A system is needed that can help medical personnel to speed up the process of diagnosing lung disease and get accurate results so that misdiagnosis does not occur. The aim of this research is to utilize the Spiking Neural Network (SNN) method for classifying lung disease from DR images. The system was created using MATLAB with the initial step of creating a read data program, namely reading DR image secondary data in .jpg format taken from Kaggle.com. This research uses DR image data totaling 200 images. Next, standardize the size to 50 x 50 pixels. Then segmenting the image divides the gray level histogram into two different parts of the image automatically without requiring user assistance to enter threshold values ​​for normal and pneumonia images. Then convert the image to 1 dimension and create a manual program for the training data using 50 normal images and 50 pneumonia images. Lastly, create a program to test the data using 100 normal images and 100 pneumonia images. Based on the results of data testing, a confusion matrix was obtained from 200 images with sensitivity of 87%, specificity of 69%, precision of 73.7288%, recall of 69%, and accuracy of 78%

Desain Didaktis Penerapan Materi Statistika Kelas IV Sekolah Dasar

This research was motivated by learning obstacles experienced by students. These learning obstacles were obtained from the results of preliminary studies on statistics material. Thus, the aim of this research is to develop a didactical design for statistical material so that it can overcome the learning obstacles experienced by students. This research is qualitative research using the Didactical Design Research (DDR) method through three stages, namely a) Prospective Analysis, b) Metapedadidactic Analysis, and c) Retrospective Analysis. Data collection in this research is interviews, tests, documentation, photos. The results of this research show that there are several LOs experienced by students in statistics material, namely that there are still some students who cannot read data and there are still some who cannot make bar charts. HLT is structured based on the LO experienced by students, namely: 1) understanding the meaning of data, 2) understanding how to read data and interpreting data in the form of bar charts, 3) understanding how to present or make bar charts. The didactic design was implemented and obtained from the results of the final identification test, there was a decrease in LO compared to the initial diagnostic test. Based on these findings, a revision of the hypothetical didactic design was carried out and a revised didactic design was obtained.

Pindel-TD: A Tandem Duplication Detector Based on A Pattern Growth Approach.

Tandem duplication (TD) is a major type of structural variations (SVs) that plays an important role in novel gene formation and human diseases. However, TDs are often missed or incorrectly classified as insertions by most modern SV detection methods due to the lack of specialized operation on TD-related mutational signals. Herein, we developed a TD detection module for the Pindel tool, referred to as Pindel-TD, based on a TD-specific pattern growth approach. Pindel-TD is capable of detecting TDs with a wide size range at single nucleotide resolution. Using simulated and real read data from HG002, we demonstrated that Pindel-TD outperforms other leading methods in terms of precision, recall, F1-score, and robustness. Furthermore, by applying Pindel-TD to data generated from the K562 cancer cell line, we identified a TD located at the seventh exon of SAGE1, providing an explanation for its high expression. Pindel-TD is available for non-commercial use at https://github.com/xjtu-omics/pindel.

Positive feedback field effect transistor based on vertical NAND flash structure for in-memory computing

The distance between memory and central processing unit has led to a memory wall. To solve it, an in-memory technology that performs both memory and computation has been studied. To realize an ideal in-memory computing, we propose a positive feedback FET based on vertical NAND flash structure that can act as a memory and perform computation. The device can reconfigure the processing operations into AND or OR operations depending on the control gate bias. It performs memory by accumulating charge in the body, and logic operations can be performed by reading data stored in the charge trap layer. After this, it can also perform a writing operation. This component enables memory and read-compute-write operations, making it capable of implementing intrinsic in-memory computing. As a result, in this study, we designed and verified a structure that implements the core principles of in-memory computing.

Analisis Dimensi Berpikir Kritikal dalam Program Membaca Ekstensif

This research aims to analyze critical thinking dimensions in an extensive reading program. The research qualitative research approach, with a case study design. The participants consist of students at the university who take part in extensive reading programs. Data were collected through reading reading logs and tests that measured students' critical thinking. The data collected from the critical thinking test was then analyzed statistically, while the log reading data was processed using a qualitative analysis approach. The results showed that there were differences in students' critical thinking test results before and after participating in the extensive reading program. This result was supported by the participants’ reading logs. The extensive reading program implemented by the researchers may not directly create significant changes in terms of average grades, but the results indicate that the program effectively improves students' overall understanding and critical thinking abilities. Therefore, although the results of the significance test are able to provide an idea of whether the changes that occur between the Pre-Test and Post-Test are random or not, the results of the Ho analysis highlight the substantial impact achieved through the treatment that the researcher provided.

Comparative analysis of metagenomic classifiers for long-read sequencing datasets

BackgroundLong reads have gained popularity in the analysis of metagenomics data. Therefore, we comprehensively assessed metagenomics classification tools on the species taxonomic level. We analysed kmer-based tools, mapping-based tools and two general-purpose long reads mappers. We evaluated more than 20 pipelines which use either nucleotide or protein databases and selected 13 for an extensive benchmark. We prepared seven synthetic datasets to test various scenarios, including the presence of a host, unknown species and related species. Moreover, we used available sequencing data from three well-defined mock communities, including a dataset with abundance varying from 0.0001 to 20% and six real gut microbiomes.ResultsGeneral-purpose mappers Minimap2 and Ram achieved similar or better accuracy on most testing metrics than best-performing classification tools. They were up to ten times slower than the fastest kmer-based tools requiring up to four times less RAM. All tested tools were prone to report organisms not present in datasets, except CLARK-S, and they underperformed in the case of the high presence of the host’s genetic material. Tools which use a protein database performed worse than those based on a nucleotide database. Longer read lengths made classification easier, but due to the difference in read length distributions among species, the usage of only the longest reads reduced the accuracy. The comparison of real gut microbiome datasets shows a similar abundance profiles for the same type of tools but discordance in the number of reported organisms and abundances between types. Most assessments showed the influence of database completeness on the reports.ConclusionThe findings indicate that kmer-based tools are well-suited for rapid analysis of long reads data. However, when heightened accuracy is essential, mappers demonstrate slightly superior performance, albeit at a considerably slower pace. Nevertheless, a combination of diverse categories of tools and databases will likely be necessary to analyse complex samples. Discrepancies observed among tools when applied to real gut datasets, as well as a reduced performance in cases where unknown species or a significant proportion of the host genome is present in the sample, highlight the need for continuous improvement of existing tools. Additionally, regular updates and curation of databases are important to ensure their effectiveness.