Minimum Time Research Articles

A new efficient decoder of linear block codes based on ensemble learning models: case of boosting

<p>Error-correcting codes are used to partially or completely correct errors as much as possible, while ensuring high transmission speeds. Several Machine Learning (ML) models such as Logistic Regression and Decision tree have been applied to correct transmission errors. Among the most powerful ML techniques are aggregation methods which have yielded to excellent results in many areas of research. It is this excellence that has prompted us to consider their application for the hard decoding problem. In this sense, we have successfully designed, tested and validated our proposed EL-BoostDec decoder (hard decision decoder based on Ensemble Learning - Boosting technique) which is based on computing of the syndrome of the received word and on using Ensemble Learning techniques to find the corresponding corrigible error. The obtained results with EL-BoostDec are very encouraging in terms of the binary error rate (BER) that it offers. Practically EL-BoostDec has succeed to correct 100% of errors that have weights less than or equal to the correction capacity of studied codes. The comparison of EL-BoostDec with many competitors proves its power. A study of parameters which impact on EL-BoostDec performances has been established to obtain a good BER with minimum run time complexity.</p>

Basketball Shooting Machine

Abstract: The machine developed by us helps basketball athletes to excel, improve and complete their training in minimum time and effort. It cuts down the need of another human who collects and passes the ball to the athlete as well as it saves the athlete's stamina by not letting him/her run continuously around the court. This machine is placed under the basketball rim where it collects the ball shot by the athlete and shoots it back towards him/her and the cycle continues.

Технология изготовления образцов из композитных материалов для проведения лабораторных испытаний

Based on the accumulated experience of the staff of the Institute of Marine Technologies, Energy and Transport of the Astrakhan State Technical University, the development of the production of various parts of ship hulls, elements of main and auxiliary power plants, including various fasteners and stanchions, using additive technology and other methods of forming composite materials has begun. During the implementation of the developed direction, issues arise of reducing the cost, weight of manufactured parts and improving the characteristics of sound absorption, sound insulation, vibration isolation, etc. Despite the fact that adhesives based on epoxy or polyester resin, according to technical documentation, are fast-curing adhesives, they are not applicable for the production of products using three-dimensional printing technologies. for several reasons. The factors preventing the use of materials for the production of such parts are listed: the minimum curing time of one layer of glue under optimal conditions is from one and a half hours or more, which significantly increases the interlayer time interval; the glue layer is not a self-supporting structure due to the fact that it is in a liquid state during the induction period; products made of this adhesive without reinforcement cannot withstand the required dynamic loads. In the process of experimental studies, a number of formulations based on polyester resin with different fillers were obtained, which have the necessary consistency to apply layers on top of the previous one without waiting for curing, and also contain the necessary amount of reinforcing material and other fillers required to solve problems related to vibration isolation and sound insulation.

Enhancing registration systems: Implementing database systems in digital boarding schools

Islamic boarding schools, known as pondok pesantren are specialized schools for Islamic education in Indonesia. With the community's increasing interest in religious education, some pesantren have begun to offer online learning opportunities. Pondok Pesantren Bayt Tamyiz is a Islamic boarding school that has started developing online pesantren education under the name Tamyiz Digittren. This program aims to enhance understanding and translation of the Qur'an using the Tamyiz method. The pondok pesantren requires significant changes in its administrative systems, including the registration process. Information system development is often challenging for the pesantren staffs and software developers. One of the fundamental difficulties lies in the user requirement process. Typically, pesantren need help to provide the information system they need. Such difficulties hinder the development process as developers must conduct numerous experiments. This research aims to design and implement a suitable database for the new student registration system with minimal information and limited time development. The research follows a database development method divided into three stages: Requirement Collection and Analysis, Conceptual Database Design, and Logical Database Design. Database development utilizes MySQL as the Database Management System (DBMS) and PHP as the programming language. This research results in a database design capable of storing and managing student data, payments, and other registration information. We expect the results of this database development to be effective in supporting the registration system and in the future development of information systems at Pondok Pesantren Bayt Tamyiz.

A Ciphertext Reduction Scheme for Garbling an S-Box in an AES Circuit with Minimal Online Time

The secure computation of symmetric encryption schemes using Yao’s garbled circuits, such as AES, allows two parties, where one holds a plaintext block m and the other holds a key k, to compute Enc(k,m) without leaking m and k to one another. Due to its wide application prospects, secure AES computation has received much attention. However, the evaluation of AES circuits using Yao’s garbled circuits incurs substantial communication overhead. To further improve its efficiency, this paper, upon observing the special structures of AES circuits and the symmetries of an S-box, proposes a novel ciphertext reduction scheme for garbling an S-box in the last SubBytes step. Unlike the idea of traditional Yao’s garbled circuits, where the circuit generator uses the input wire labels to encrypt the corresponding output wire labels, our garbling scheme uses the input wire labels of an S-box to encrypt the corresponding “flip bit strings”. This approach leads to a significant performance improvement in our garbling scheme, which necessitates only 28 ciphertexts to garble an S-box and a single invocation of a cryptographic primitive for decryption compared to the best result in previous work that requires 8×28 ciphertexts to garble an S-box and multiple invocations of a cryptographic primitive for decryption. Crucially, the proposed scheme provides a new idea to improve the performance of Yao’s garbled circuits. We analyze the security of the proposed scheme in the semi-honest model and experimentally verify its efficiency.

Investigation of compound occurrence of storm surge and river flood in Mikawa Bay, Japan, using typhoon track ensemble experiments

The risk of high river flow and peak storm surge superimposing in the estuary of small-and medium-sized rivers can be more severe during typhoon landfalls than at other times. In this study, 30 cases of typhoon track ensemble experiments for five rivers in Mikawa Bay, Japan, were evaluated using an atmospheric-river-ocean model. The results showed that storm surges coincided with high river flows when typhoons crossed the western side of Mikawa Bay, with the highest potential observed when typhoons passed northwest of the Bay. The average time discrepancy for the five rivers between the storm surge occurrence and increased river flow in the minimum time difference case was 48 min at the river mouth. Distinct trends in the time difference between a storm surge and river flow were observed for each river mouth. The order of time differences corresponded to the drainage area scale: Toyo (188 min), Umeda (112 min), Otowa (114 min), Sana (96 min), and Yagyu (88 min). Time difference trends also varied based on the distance between the typhoon center and each river mouth. For Toyo River, storm surges and high river flows did not substantially overlap, regardless of distance. However, for medium-and small-sized rivers, the time difference increased proportionally with the distance from the typhoon. The Yagyu River showed the highest potential for compound occurrence of a storm surge and river flood because of the synchronization of storm surge peaks and high river flow peaks, with a time difference of 1–2 h for small-and medium-sized rivers within 100 km2 of the basin.

Temperature control by simulated adaptive layer times in powder bed fusion processes

AbstractIn this work, a simulation-based thermal management model for metal parts produced through powder bed fusion (PBF) is proposed. PBF is an additive manufacturing technique that employs a high-energy beam to selectively melt and fuse powder particles layer by layer. The productivity and efficiency of PBF processes can be significantly increased using multi-laser systems with larger build volumes. However, this approach affects the parts thermal history, which can significantly impact their mechanical properties, microstructure, and defects. To address this issue, an algorithm has been developed to calculate adaptive cooling times reaching predefined temperatures at the end of each layer. The algorithm is used in a fast thermal process simulation using layer lumping. The simulation model is applied to a modern multi-laser machine, and the effectiveness of the adaptive cooling times and minimal layer times is evaluated. The results indicate that lower maximum temperatures can be achieved in less manufacturing time with adaptive cooling times than with minimal layer times. However, the significant increase in manufacturing time highlights the need for active cooling systems to utilize multi-laser machines fully. In summary, this paper presents a significant contribution to the field of additive manufacturing, emphasizing the importance of thermal management in ensuring the quality and performance of metal parts produced through PBF.

A deep learning framework for quality control process in the motor oil industry

Given the advancements in the modern world, using Multivariate-Multistage Quality Control (MVMSQC) patterns in continuous production industries is deemed crucial and essential. This study examines the importance and necessity of Multivariate-Multistage Quality Control in manufacturing industries, focusing on motor oil production. Motor oil quality significantly influences engine performance. Thus, the primary objective of this research is to enhance accuracy in detecting faults in quality variables, utilizing deep learning algorithms for visual quality control of data, a facet often overlooked in classical statistical methods and reducing process control time. Combining deep learning algorithms such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for controlling numerical variables, and Residual Networks (ResNet) and Dense Convolutional Networks (DenseNet) for controlling image variables have been employed. Additionally, the Honey Bee Mating Optimization Algorithm (GBC) has been utilized to tune the parameters of the LSTM-CNN and ResNet-DenseNet deep learning algorithms. Combining heuristic algorithms and deep learning algorithms enhances the performance of the final models in quality control processes. A case study in the motor oil production industry is examined to demonstrate the real-world application of the proposed model. The proposed LSTM-CNN hybrid algorithm outperforms individual CNN and LSTM algorithms in fault detection, improving performance by 15% and 8%, respectively. Furthermore, in visual components, the proposed ResNet-DenseNet hybrid algorithm has shown higher accuracy than ResNet and DenseNet algorithms, improving performance by 10% and 15%, respectively. This research significantly contributes to developing Multivariate-Multistage Quality Control in manufacturing industries by employing deep learning methods. The primary aim is to identify faults in quality components and utilize deep learning algorithms for visual quality control of data, a facet often overlooked in classical statistical methods. The research significantly advances multivariate-multistage quality control in manufacturing industries by leveraging deep learning methods. It addresses detecting complex nonlinear patterns in large-scale data with high flexibility and minimal time.

Tyre and brake thermal management in NASCAR

The need to manage race car tyres is routine faire and several academic papers have already appeared on this topic. We contribute to this literature with refined tyre grip and wear-rate models. Less well known is a methodology for managing the thermal transients in the car's braking system. This knowledge gap is addressed with a physically-based disc-and-calliper brake-heating model. The parameters in each of the models are optimised against measurement data captured on an instrumented Generation 7 (Gen 7) NASCAR driven on the Darlington speedway. These models are replicated on each corner of the vehicle. The remainder of the paper is directed to studying the minimum lap time performance of a Gen 7 NASCAR that is subject to tyre wear and temperature variations; brake heating influences are emphasised. Due to their novelty, the results focus on the performance impact of temperature variations and constraints on the front brake disc temperatures. Constraints on the brake temperatures are introduced to protect the brakes from fade and thermally-induced damage.

IMPRESS-Norway: improving public cancer care by implementing precision medicine in Norway; inclusion rates and preliminary results.

In Norway, comprehensive molecular tumour profiling is implemented as part of the public healthcare system. A substantial number of tumours harbour potentially targetable molecular alterations. Therapy outcomes may improve if targeted treatments are matched with actionable genomic alterations. In the IMPRESS-Norway trial (NCT04817956), patients are treated with drugs outside the labelled indication based on their tumours molecular profile. IMPRESS-Norway is a national, prospective, non-randomised, precision cancer medicine trial, offering treatment to patients with advanced-stage disease, progressing on standard treatment. Comprehensive next-generation sequencing, TruSight Oncology 500, is used for screening. Patients with tumours harbouring molecular alterations with matched targeted therapies available in IMPRESS-Norway, are offered treatment. Currently, 24 drugs are available in the study. Primary study endpoints are percentage of patients offered treatment in the trial, and disease control rate (DCR) defined as complete or partial response or stable disease in evaluable patients at 16 weeks (W16) of treatment. Secondary endpoint presented is DCR in all treated patients. Between April 2021 and October 2023, 1,167 patients were screened, and an actionable mutation with matching drug was identified for 358 patients. By the data cut off 186 patients have initiated treatment, 170 had a minimum follow-up time of 16 weeks, and 145 also had evaluable disease. In patients with evaluable disease, the DCR was 40% (58/145). Secondary endpoint analysis of DCR in all treated patients, showed DCR of 34% (58/170). Precision cancer medicine demonstrates encouraging clinical effect in a subset of patients included in the IMPRESS-Norway trial.

Predictive Analysis for Personalized Machine: Leveraging Patient Data for Enhanced Healthcare

This research explores predictive analysis for personalized machine: leveraging patient data for enhanced healthcare. By leveraging the power of information and analytics, the healthcare industry can be driven towards a more patient-centric, proactive model that enhances outcomes and improve the overall quality of care. The objectives of the study are to: determine the significance and challenges of predictive analytics in healthcare, ascertain the data analytics techniques used in healthcare to enhance patient care, find out how predictive analytics can be applied for enhanced healthcare, and determine the ethical considerations associated with healthcare predictive analytics. This study employs the case study approach and experimental design. The study analyzes case studies of real-time deployment of predictive analytics models in healthcare centers, examines how these models enhance the healthcare delivery in those centers. Experiments were also conducted to understand how predictive analytics works. The C4.5 learning algorithm was employed to predict the presence of chronic kidney disease (CKD) in patients and differentiate between those not affected by the condition. The C4.5 classifier shows reasonable strength, evident in the large number of rightly classified occurrences (396) and a low misclassification of only 4 occurrences. This is further demonstrated by a low error rate of 0.37, as shown in table 5. The prevalence of this algorithm is emphasized by the large value of KS (0.97), indicating the classifier’s ground-breaking accuracy and performance. The performance of C4.5, featured by its minimal execution time and accuracy, puts it as a decent classifier. This characteristic makes it specifically well-suited for application in the healthcare sector, particularly for tasks involving prediction and classification. The application of data analytics methods for predictive analysis holds significant benefits in the health sector, as it gives us the power to predict and address potential threats to human health, covering different age groups, from the young ones to the elderly. This proactive method enables early disease detection, helping in timely interventions and contributing to better decision-making.

Influence of Iba Concentrations on in Vitro Rooting of Cucumber Varieties

In vitro root induction was observed in cucumber as influenced by varieties and IBA concentrations. Four cucumber varieties viz. Shila, Green Field, Shital and Shahi-50 and three IBA concentrations viz. 0.0 (Control), 1.0, and 2.0 mg l-1 were used in this investigation. The Shital variety with in maximum number and the highest percentage of shoot produced roots. No root induction was observed when medium was auxin free. Higher concentration of IBA resulted in higher number (3.94) and percentage (98.65 %) of root. Half strength MS medium when fortified with 2.0 mg l-1 IBA took minimum time (6.88 days). Combined effect of varieties and IBA concentrations were found significant in all the parameters studied. The highest number (19.54) of roots were obtained from variety Shital when they were interacted with higher IBA concentration (2.0 mg l-1) followed by the interaction of same variety with lower concentration (19.18). The highest root length (8.80 cm) was found in the interaction of Shila x 2.0 mg l-1 IBA and the lowest root length (6.31 cm) was found in the interaction of Shahi-50 x 1.0 mg l-1 IBA. The Chittagong Univ. J. B. Sci.,Vol. 7(1 &2):01-10, 2012

Advanced integrative molecular platform for high-throughput screening of drug-resistant tuberculosis

A real time-polymerase chain reaction-based test in lyophilized form, was developed to simultaneously identify Mycobacterium tuberculosis complex (MTC) by targeting IS6110, rrs as dual markers, as well as mutations causing rifampicin and isoniazid resistance. The test was evaluated for pulmonary and non-pulmonary specimens from sample isolation to PCR analysis. The test demonstrated limit of detection of 25 CFU/mL for MTB, 200 CFU/mL for rpoB and inhA/katG targets with >95 % CI. The specificity for MTC was supported by a comprehensive clinical validation (n = 100). This load-and-go molecular platform, with features of high throughput, long shelf-life, room temperature storage provides simultaneous detection of MTC and its drug-resistant mutations in minimal time. The test named “PathoDetect TM MTB-RIF and INH resistance detection kit” has been approved by Central Drugs Standard Control Organisation, Indian Council of Medical Research and would have implications for tuberculosis elimination programs.

Input-to-state stability of discrete-time impulsive switched delayed systems with delay-dependent impulse under two asynchronous cases

This paper studies a class of discrete-time impulsive switched delayed systems with delay-dependent impulses, aiming to solving the input-to-state stability (ISS) problem in the case of asynchronous switching signal between subsystem and controller and asynchrony of switching signals and impulsive signals. Among them, the delay effect is handled by a new Lyapunov-Krasovskii function divided into a delay-dependent part and a delay-independent part. In contrast to admissible edge-dependent average dwell time (AED-ADT) methods that address switching signals, we propose admissible edge-dependent average impulsive interval (AED-AII) to deal with delayed impulses, thereby establishing the relationship between these methods and the decay rate. Meanwhile, a stabilizing controller for discrete impulsive switched linear delayed systems is proposed and the minimum average dwell time and controller gain are obtained. Compared with the previous work, the parameters of Lyapunov functional relation depend on the directed edges; the operation of subsystem is divided into asynchronous time and synchronous time, which are less conservative; the combination of AED-ADT and AED-AII can be applied to a wider range of impulsive switched systems. Finally, two examples are given to show the effectiveness of the results.

Foraging Attributes of Insect Pollinators on Tulsi (Ocimum basilicum)

Pollination is an intricate relationship between plants and pollinators; this interaction is the best example of mutualism where pollinators utilize floral rewards while pollination accomplished on plants. Pollination ecology of tulsi (Ocimum basilicum) is poorly understood. Foraging attributes: foraging rate, foraging speed and transit time of pollinators are key performance indicator to determine their pollination efficiency. Foraging speed and foraging rate were recorded during blooming period with the help of stopwatch and transit time was calculated from foraging rate and foraging speed. The performance of seven pollinators; Apis florea, Amegilla cingulata, Megachile femoratella, Megachile sp., Apis cerana, Apis dorsata and Apis mellifera were evaluated on the basis of foraging attributes. Foraging rate, foraging speed and transit time varied during the blooming period week to week and pollinator species to species. The maximum foraging rate was observed with A. mellifera, maximum foraging speed was observed with A. florea and minimum transit time as well as wasted minimum time was calculated with A. mellifera. The maximum foraging rate, foraging speed and minimum transit time were observed with all the pollinators at the peak of blooming period. Based on foraging attributes A. mellifera performance was better than other pollinators on Tulsi.

Age Dating of Repurified and Mixed Plutonium Using Machine Learning

Age dating a sample of nuclear material is a key part of predetonation technical nuclear forensics. As plutonium stockpiles age, they are more likely to require repurification and mixing to remove in-grown daughter products and maintain a consistent product. Existing age-dating techniques do not adequately address this problem. Four models were trained using machine learning techniques to determine (1) if a sample of weapons-grade plutonium had been repurified, (2) the elapsed time after repurification, and (3) the minimum and maximum elapsed times between repurification and its initial separation/purification/fabrication. The trained models predicted the repurification status with 99% accuracy, the age after repurification with a root-mean-square error (RMSE) of 0.34 years, and the minimum and maximum ages before repurification with RMSEs of 4.66 and 9.34 years, respectively. Age dating plutonium provides valuable insight into the country and possibly the facility of origin of the material, which is one tool to deter state-sponsored nuclear terrorism.

Phytomediated zinc oxide and sulfur nanoparticles for management of soft-rot causing pathogenic fungi in ginger

Diseases management has been the greatest challenge to agriculture leading to huge crop failure, represented in low-profit production. Phytopathogenic fungi are among pathogens causing a variety of agricultural diseases. Various nanoparticles (NPs) have previously been employed to combat phytopathogenic fungi. Post-harvest deterioration due to fungal invasion is the most imperative cause of loss of ginger (Zingiber officinale) production. In the present study, four fungal pathogens isolated from soft-rot infected ginger were identified as Pythium spp. (isolate I and II), one Fusarium sp. (isolate III) and one Aspergillus sp. (isolate IV). Moringa oleifera mediated zinc oxide (ZnONPs) and sulfur nanoparticles (SNPs) were characterized by UV–Visible spectrophotometry and showed absorption maxima at 356 nm and 295 nm. Average size of NPs under Nanoparticle Tracking Analyzer was 78 nm and 26 nm respectively, confirmed by FESEM. Stable NPs with zeta potential of −14.1 mV (Standard deviation = 4.51 mV) and −21.1 mV (Standard deviation = 9.56 mV) were synthesized. FTIR detected the presence of various functional groups in NPs. In vitro antifungal activity was assessed by Kirby-Bauer disc diffusion assay and MIC values compared to antibiotic ketoconazole and fungicide mancozeb. Time kill assay represented the minimum time required by selected NPs to inhibit the growth of test pathogenic fungi. ZnONPs and SNPs can be used for further antifungal studies in the field as novel nanofungicides. Extrapolated and uncontrolled use can lead to the inhibition of germination, reduction in photosynthesis rate, and disruption of the plant roots. Thorough small-scale experimentation is necessary before commercialization.

Minimal time sampled-data controls for the ordinary differential system

In this paper, we design a minimal time optimal control problem for an ordinary differential system with sampled-data controls, and use it to approximate a time optimal control problem for the ordinary differential system with distributed controls. We find connections among this problem, a minimal norm sampled-data control problem and a minimization problem, and obtain some properties on these problems. Based on these, we not only build up error estimates for optimal time and optimal controls between the minimal time sampled-data control problem and the minimal time distributed control problem, in terms of the sampling period, but we also prove that such estimates are optimal in some sense.

Geographic Accessibility of General Practitioners by Car and Public Transport - A Study Using the Minimum Travel Time Method in a Rural Area in the North-East of Germany

Basic medical care should be accessible to all people without restriction. However, the distribution of practices in rural areas is often limited to the centers, meaning that distances have to be covered. The Federal Joint Committee (G-BA), the highest decision-making body of the joint self-government of physicians, dentists, hospitals and health insurance funds in Germany, only states for cars that 95% of the population should be able to reach general practitioners (GPs) in less than 20 minutes on average. The aim of this study was to determine the geographic accessibility of GP practices by car and public transport. A cross-sectional study was conducted in a rural area in the north-east of Germany called Neustrelitz Central Zone and reported according to the STROBE statement. Geographic data of OpenStreetMap and the Federal Agency for Cartography and Geodesy and GP practices data of the Associations of Statutory Health Insurance Physicians Mecklenburg-Western Pomerania and Brandenburg were used. Based on the method of minimum travel time, the required travel times by car and public transport from the local centers to the three nearest physician locations were determined using various online tools. On average, inhabitants need 10.7 minutes to reach the three nearest GP practices by car, up to a maximum of 19 minutes. The average travel time by public transport for an appointment at 10 a.m. is four hours and twelve minutes. While it is possible for all municipalities (and their inhabitants) to reach the nearest GP practices in time for an appointment at 10 a.m. and to complete the return journey on the same day, this is no longer possible for 35% of municipalities (and their inhabitants) for an appointment at 3 p.m. and for 53% for an appointment at 5 p.m. In the Neustrelitz Central Zone, the accessibility of GP practices by car is ensured in accordance with the G-BA statements. On the other hand, the accessibility of GP practices by public transport can be a problem for people who do not have their own car. This could be remedied, for example, by expanding eHealth or mobile medical practices.

Coconut Husk Waste‐Derived Nitrogen‐Doped Mesoporous Carbon Nanomaterial as an Efficient and Sustainable Supercapacitor

Sustainability and waste management are equally important in the era of growing energy demands. This study focuses on integrating strategies that balance energy efficiency, reduce waste generation, and promote sustainable practices. The coconut husk is processed and converted into mesoporous carbon nanomaterial through hydrothermal‐assisted thermal annealing. Nitrogen is doped through melamine to increase reactive sites and hence the catalytic activity. The sphere‐like morphology was confirmed by field emission scanning electron microscope and high‐resolution transmission electron microscope images indicate a carbon dot‐like structure. The high Brunauer‐Emmett‐Teller surface area achieved for this nanostructure is 1383.40 m2 g−1, with pore width and volume at 2.2660 nm and 0.2995 cm3 g−1, respectively. The as‐synthesized nanostructure is explored for its electrochemical performance as electrodes for the supercapacitor application, where materials achieve a wide potential window (−1.4 to 1.4 V) in the minimum time of 50 s at the current density of 0.6 A g−1. Moreover, the highest specific capacitance obtained from the nanomaterial is 115.39 F g−1 in the potential window and current density of 2 V and 0.6 A g−1, respectively. The energy density, power density, and coulombic efficiency at the set parameter are 62.31 Wh Kg−1, 1166.4 W Kg−1, and 79.61%, respectively. Finally, the practical device is assembled with the nanomaterial, where a red LED bulb glows after 1 min of charging.