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With the high-paced change in the world, the market trends are changing fast and so do the technological innovations. The market is constantly moving and being affected by a number of external factors, making it difficult for investors to make decision. To solve this problem, a technique called Algorithmic Trading is being implemented widely. It refers to use of computer algorithms to make trading decisions without human intervention. In this paper, the algorithm used to predict the stock prices is the LSTM model which is a methodology of neural networks.The existing models are less accurate as they do not take technical factors into consideration. LSTM model, having error of only 0.00036%, overcomes this limitation and also takes temporal factor into consideration.

Abstract Construction industry plays a significant role in the economic development of the nation. Due to site and other constraints, concreting is preferred by ready mix concrete (RMC) plants. It is very well-known fact that the construction industry immensely contributed for CO2 emission mainly from cement-based materials. One of the way to achieve smart city objective to effectively manage ready mix concrete plants. While opting for RMC over site mixing offers various advantages. In this study, key factors governing the sustainability and energy consumption of RMC plants are identified. This study investigates the efficiency and sustainability of RMC production in Maharashtra, suggesting that its efficiency hinges on various factors. Through a constructivist perspective, the research examines twelve RMC plants in Pune mainly and other parts of Maharashtra, finding that power supply, plant output, and capacity require sustainability. While raw material supply is deemed sustainable, management methods and product control were found lacking. The study identifies challenges affecting demand, such as regulatory issues, management issues and technological limitations. The conclusion emphasizes the need for alternative and sustainable power sources, skilled labor, and improved business processes to enhance RMC production efficiency in Maharashtra, recommending measures like waste reuse and innovative advancements. This study will help the stakeholders in prioritizing the resources and process towards more sustainable way.

Commercially available crowdsourced connected vehicle (CV) trajectory data have recently been used to provide stakeholders with actionable and scalable roadway mobility infrastructure performance measures. Transportation agencies and automotive original equipment manufacturers (OEMs) share a common vision of ensuring the privacy of motorists that anonymously provide their journey information. As this market has evolved, the fleet mix has changed, and some OEMs have introduced additional fuzzification of CV data around 0.5 miles of frequently visited locations. This study compared the estimated Indiana market penetration rates (MPRs) between historic non-fuzzified CV datasets from 2020 to 2023 and a 5–11 May 2024, CV dataset with fuzzified records and a reduced fleet. At selected permanent interstate and non-interstate count stations, overall CV MPRs decreased by 0.5% and 0.3% compared to 2023, respectively. However, the trend in previous years was upward. Additionally, this paper evaluated the impact on data characteristics at freeways and intersections between the 5–11 May 2024, fuzzified CV dataset and a non-fuzzified 7–13 May 2023, CV dataset. The analysis found that the total number of GPS samples decreased 10% statewide. Of the evaluated 54,284 0.1-mile Indiana freeway, US Route, and State Route segments, the number of CV samples increased for 33.8% and decreased for 65.9%. This study also evaluated 26,291 movements at 3289 intersections and found that the number of available trajectories increased for 28.3% and decreased for 70.4%. This paper concludes that data representativeness is enough to derive most relevant mobility performance measures. However, since the change in available trajectories is not uniformly distributed among intersection movements, an unintended sample bias may be introduced when computing performance measures. This may affect signal retiming or capital investment opportunity identification algorithms.

Intersection safety has been traditionally evaluated using three to five years of crash data. Recent literature suggests that connected vehicle (CV)-derived hard braking (HB) events can provide a surrogate for crashes with only a few weeks or months of data collection. This study used CV trajectories to derive HB events. Then, the HB events were normalized as the ratio of HB events to sampled CV trajectories. The normalized HB ratios were evaluated and compared at 435 signalized intersections, roundabouts, and all-way stops in Indiana. The analysis showed that signalized intersections and roundabouts had the highest counts of HB events, and all-way stops had the highest HB ratios. Through movements at signalized intersections showed the lowest HB ratios, whereas left turns at all-way stops had the highest ratios. A density analysis of the geospatial occurrence of HB events concluded that they tend to occur closest to the intersection center at all-way stops, but are more evenly distributed at signalized intersections. Additionally, a speed analysis indicated that HB events at signalized intersection through movements tend to occur at higher speeds, roughly between 26 and 36 MPH, perhaps due to the driver reaction during the onset of yellow. The findings presented in this study provide transportation agencies with insights on the occurrence of normalized HB ratios at three different intersection types. The data provided in this paper provide a framework for agencies to use HB ratios to screen different types of intersections for further evaluation.

Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively.

The path of totality for the solar eclipse on April 8, 2024, passed through 13 states in the United States from Texas on the western end through Maine on the eastern end. This paper reports on the use of connected truck data to evaluate the impact of the solar eclipse on traffic mobility on Interstate routes in those thirteen states. Approximately 192 million connected truck data records from more than 240,000 trucks were systematically analyzed. Overall, eleven states (Arkansas, Illinois, Indiana, Kentucky, Maine, Missouri, New Hampshire, New York, Ohio, Pennsylvania and Vermont) were observed to have a total week over increase in the number of mile hours of Interstate operating below 45 mph by 66%. The other two states, Texas and Oklahoma were observed to have an actual reduction in week over congestion during the eclipse by 35%. Indiana had the highest increase in congestion on eclipse day by 856 mile-hours and significant mobility impacts were observed the day before and the day after the eclipse as well. This was likely due to the number of large viewing events distributed across the region of totality. A combination of roadside ITS cameras and on-vehicle dash camera images were used to qualitatively assess the prevailing roadway conditions and provide context. In addition to demand induced congestion in areas within or adjacent to totality, the dash cameras identified a variety of unstructured roadside parking adjacent to or within areas of totality.

A series of transition metal complexes of Mn(II),Co(II), Ni(II), Cu(II), Zn(II) and VO(II) have been prepared by using the Schiff base ligand (L) derived from 4-aminoantipyrine and 5-chlorosalicylaldehyde. The structural properties of Schiff base ligand were characterized by mass, FT-IR, UV– –Vis, 1H-NMR spectroscopy, etc. Also, metal complexes were studied by P- -XRD, elemental analysis, thermogravimetric studies along with various biological activities. The micro analytical data revealed that, the metal complexes have 1:1 stoichiometry composition of M:L. Generally, it is observed that prepared metal complexes show better antifungal, antibacterial and anticancer activities than its Schiff base ligand.

International trade is a critical part of the United States economy. Land border crossings between the United States and Mexico accounts for a large proportion of the USD 779 billion in trade between these two countries. Monitoring and managing the operations of these land border crossings is critical for ensuring efficient trade and providing appropriate security. This paper examines the opportunity to use connected vehicle data to monitor the travel time delay of passenger vehicles crossing the border for system level assessment across 26 border crossing locations over an analysis period of 25 days in August 2020. A sample size of 51,341 trips from the US to Mexico and 41,708 trips from Mexico to the US were used in this study. Furthermore, 97% trips to the US and 76% trips to Mexico experienced delays. The average delay was 34 min for trips to the US compared to only 2 min for trips to Mexico. In terms of the predictability of border crossing times, there was also substantial variation by direction. The interquartile range of vehicle delay from the US to Mexico was 2 min, while the interquartile range of delay for vehicles travelling from Mexico to the US was 46 min. Border crossings were also ranked using four performance metrics—trip counts, median delay, delayed trip counts and total delays in vehicle hours. Methods for summarizing delay trends by time of the day and day of the week to identify time windows of interest are also presented. Land border crossing operations have a significant influence on security and economic efficiency. We believe the techniques presented in this paper provide a scalable methodology for providing near real-time factual data on border crossing delays that provide important information for land border transport-managing stakeholders to make informed management decisions that balance security and economic efficiency.

Introduction: Understanding pulmonary function at various phases after coronavirus disease 2019 (COVID-19) infection is critical for determining the exact pathophysiological mechanism of COVID-19. Research Question: What is the correlation between spirometry indices and clinical indicators in COVID-19 patients over a 6-week follow-up? Objectives: 1) To assess deterioration or improvement in spirometry parameters including forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and ratio FEV1/FVC in COVID-19 patients. 2) To study the correlation between FVC, FEV1, and FEV1/FVC with oxygen saturation and clinical findings. Materials and Methods: A prospective observational study was conducted for a 6-week period among 25, COVID-19 patients who were either asymptomatic or mildly symptomatic. Each patient received a home-use-connected spirometer—SpiroPRO®, a pulse oximeter, and a thermometer from Briota Technologies Pvt Ltd. (BRIOTA). Patients and healthcare professionals were given training for performing spirometry twice a day as well as access to mobile apps was provided. Spirometry indices, patient symptoms, and vital statistics were used to calculate the VIEW™ score using machine learning algorithms. Result: The Bland–Altman plots showed that FEV1 reduced slightly up to 21–28 days and comes back to normal around 42 days. VIEW™ score increased up to day 21 and then decreased toward day 42. An increase in VIEW™ score increases the risk of COVID-19 complications. VIEW™ score and FEV1 showed a significant correlation. Conclusion: Home-based spirometry acts as an effective tool for COVID-19 patients to predict lung complications and also promote self-monitoring thereby reducing the burden on the health system.