The management of observing proposals is a critical operational component for modern astronomical facilities. As missions grow in complexity, the demand for efficient, fair, and adaptable proposal handling systems is increasingly pressing. Existing systems are often monolithic and tightly coupled to a specific observatory, lacking the flexibility to be easily adapted. This paper introduces AstroPropose, a novel, general-purpose framework for creating and managing astronomical observing proposal systems, derived from the architecture of the operational Einstein Probe Observing Proposal System (EOPS). AstroPropose is centered on a powerful visual workflow engine, enabling administrators to define and deploy entire proposal workflows through a graphical interface. Key features include a dynamic form builder, a configurable workflow engine, and a flexible role-based access control (RBAC) system. We present the complete architecture and data model. As validation, we detail how the framework’s design principles are embodied in EOPS, which has successfully managed two annual proposal cycles and handles daily time-critical Target of Opportunity (ToO) submissions for the Einstein Probe mission. To further demonstrate the framework’s generalizability, we present a second case study: its adaptation as the proposal management prototype for the Chinese Space Station Telescope (CSST), a multi-instrument survey mission with a dual-phase review process. AstroPropose, validated through the successful operation of EOPS and the rapid prototyping of the CSST system, is being prepared for an open-source release to the astronomical community.

The tremendous growth of the population has led to a world of unclean and unhygienic environments. The primary cause of pollution nowadays is garbage overflow. Cleaning the dustbin is an important process that has to be done regularly, but due to the inconsistency in the filling of waste, sometimes it overflows even before the next cleaning process arrives. It is also observed that the garbage gets accumulated due to the irregular removal of garbage present in the dustbin. Sometimes, dustbins are kept open, which produces a bad smell, and also when the dustbin is filled directly, picking up the waste can lead to many diseases. So it is highly essential to take utmost care of the waste being dumped. Hence, a new proposal system is being introduced where the lid of the bin opens when waste is detected by an Ultrasonic Sensor, and it will be closed. There will be continuous level monitoring of the garbage, which can be seen in the mobile application. Here, the dustbins are provided with different IDs, when a dustbin is full message would be sent to the respective authorities indicating the ID of the Dustbin. It has an additional feature where the polybag inside the bin is sealed automatically when the bin is full. The UV Sterilization feature is also used to kill the germs.

Procurement of educational facilities and infrastructure is a strategic component in education management that contributes to the quality of learning. This study aims to analyze the procurement management of educational facilities and infrastructure in supporting the quality of learning in public madrasahs. The research used a qualitative approach with a case study design. Data were collected through in-depth interviews, observations, and documentation studies involving the madrasah head, deputy head of curriculum, and teachers. Data analysis was done thematically through the stages of data reduction, data presentation, and conclusion drawing. The results show that the procurement of facilities and infrastructure is carried out systematically through the stages of planning, proposing, budgeting, implementing, and evaluating and maintaining which is carried out on an ongoing basis by involving various stakeholders and referring to applicable regulations. The planning and proposal system is integrated with learning needs, while budget management utilizes multi-source funds. Procurement implementation applies a tender mechanism to ensure efficiency, transparency and accountability. The research findings confirm that the implementation of good procurement governance principles contributes to the effectiveness of learning and improving the quality of education services in public madrasahs, and serves as a reference for education unit managers in designing a planned, accountable and quality-oriented facilities and infrastructure procurement system.

It is relatively well known that the technology that can be integrated by an unmanned marine surface vehicle is very diverse and partly similar to that of autonomous land vehicles (such as GNSS satellite positioning systems), but also specialized (for example, dedicated narrowband radio systems for communication between various entities, including ships, or for connecting to various services, such as the Remote Operations Centre - ROC). Therefore, a natural step would be to integrate other technologies that have just started to be used by the automotive industry, such as V2X (Vehicle to Everything), which defines the standard for achieving the connection between the vehicle and various other entities, such as another vehicle or infrastructure. The present work attempts to integrate V2X technology in the maritime domain in various use cases that provide for the positioning of the marine vehicle even in the absence of the GNSS signal or when it is jammed or spoofed. In this way, we analyse the standardization of 5G technology for maritime communications and services, but also the research direction for increasing the security of the interconnection of autonomous maritime vessels, which will be addressed in subsequent works.

This article examines the specifics of business digitalization processes through the lens of the national project "Data Economy and Digital Transformation of the State". The relevance of this study stems from the need to overcome systemic barriers to the digital transformation of business in Russia and strengthen its role within government initiatives. The article substantiates that, despite the stated goals, there is a gap between national projects and the actual needs of businesses, and also notes the limitations of the indicators used to assess digitalization. Furthermore, the authors highlight key challenges of the current situation: insufficient funding, a shortage of qualified personnel, resistance to change, and a lack of trust in digital technologies. The methodological basis of the study includes a combination of scientific methods (axiomatic reasoning, theorization, abstraction, classification, modeling, and systematization), which allowed for a comprehensive analysis of the conceptual framework of "business digitalization" from the perspective of scientific, normative, systemic, conceptual, and functional approaches. The authors conclude that the national project "Data Economy and Digital Transformation of the State" in its current form is insufficiently focused on directly supporting business processes. The novelty of this study lies in the development of a specific system of proposals for integrating business into the national project. The findings include a step-by-step action plan that includes assessing the current state of digitalization, gathering business needs, launching pilot programs, implementing investment tax deductions, and developing a system of regional performance indicators. The study's findings emphasize the need to shift the focus from creating general conditions to targeted support for business digital transformation through the proposed measures, which will contribute to achieving the national project's goals.

Intra-Cytoplasmic Sperm Injection (ICSI) is one of the most common infertility treatments in which ovarian stimulation is carried out to extract the eggs from the ovaries. There are three, short, long, and pure treatment protocols of ovarian stimulation that vary by the type of medicine, the dosage of medicine, and the treatment term.Today, physicians choose an appropriate treatment protocol based on the patient's condition, such as age, and hormonal condition. This could be a relatively subjective and inaccurate method, particularly if the physician is not highly experienced. The present study investigates whether a decision support system can propose a more objective treatment protocol based on the patients’ data and data mining methods like logistic regression, decision tree, and SVM. Such a system draws upon classification methods to propose proper treatment protocols for ICSI. Moreover, a separate module was developed to calculate the success rate of the proposed protocols. The system was tested with real data of treated patients at a Hospital in Tehran, Iran. The results showed the proposed system can predict the most proper treatment protocol with an accuracy of 81.90%. The proposed system can help inexperienced physicians to feel more confident about their advice.

Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing power consumption and read-out bandwidth. This article presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high-accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a $5.5\times$5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data.

This paper develops an integrated smart cutting parameter selection system for pocket milling. Currently, general CAD/CAM systems generate pocket milling tool paths according to user-designated cutting parameters typically including cutting depth, feed rate, and spindle speed. Pocket milling is often used for rough machining, and shorter cutting times are used to increase productivity. Although shorter machining times can be accomplished with deeper cutting, it may result in unstable cutting (chatter). Stability lobe diagrams (SLD) generated from cutting and tapping tests on selected machine tools describe the relationship between cut depth and spindle speed. However, despite offering useful information for selecting cutting parameters for chatter-free cutting, SLD has not been integrated with existed CAD/CAM systems. Therefore, this study integrates SLD in a CAD/CAM system to effectively determine the optimum cut depth and spindle speed for a theoretically chatter free pocket milling tool path. A case study is used to verify the proposed system integration.

This article is the last of three that collectively explore solutions to the problem of re-generative energy distribution for its rational use. The solution is achieved through construc-tive-production modeling. Previously, a general constructive-production model of a direct current traction power supply section was developed and enriched with information on the attribute values of its con-stituent elements. In this work, based on expert data, a system for managing the distribution of regenerative energy is formed using constructive-production modeling and fuzzy logic. The developed model allows the formation of a text file in FTL format, which is used in the fuz-zyTECH system. FuzzyTECH, in turn, implements fuzzy inference mechanisms, considering the state of electrical equipment and the current situation on the power supply section, to effi-ciently distribute regenerative energy by controlling switches at direct current power supply substations. This approach is illustrated using a schematic of a linear power supply section with three substations and two trains. The developed models open new opportunities for improving energy efficiency, particu-larly in railway and urban public electric transport. The primary tasks of the system include identifying the existing traction substation equipment and assessing its technical characteris-tics, as well as optimizing the use of regenerative energy. Moreover, the proposed approach can be useful for solving issues related to the design of traction power supply systems, en-hancing their reliability, and reducing energy losses. Additionally, the implementation of fuzzy logic methods and constructive-production modeling not only improves the management of regenerative energy distribution but also cre-ates a universal approach that can be adapted to other types of electric transport. The pro-posed system has the potential for integration with intelligent networks and modern energy-saving technologies, opening new perspectives for the development of efficient transportation solutions.

A solid-state sensor was fabricated by a spontaneous electrochemical deposition of polytyramine (Ptyr) film onto a glassy carbon electrode (GCE) which was further peripherally supported by gold nanoparticles (AuNPs). The surface materials of the developed sensor (AuNPs.Ptyr-GCE) were characterized by X-ray photoelectron spectroscopy (XPS), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The rate constant of charge transfers (kct) of the as-prepared sensor (8.77 × 10-4 cm/s) was evaluated by fitting the charge transfer resistance (Rct) data in the presence of ferric-ferrous hexacyanide redox couple solution, [Fe(CN)6]3-/4-. The voltammetric behavior of norepinephrine (NOR) was confirmed to follow an irreversible reaction mechanism at which the estimated diffusion coefficient value was 7.39 × 10-5 cm2/s. The sensor showed a large enhancement on NOR oxidation and comparatively lowered its detection limit (DL3s) to 0.130 mM (22 ppb). It was also applied for selective determination of NOR in the presence of high concentrations of ascorbic acid (AA) and uric acid (UA). The interference study highlighted the great stability of the proposed sensor by generating a similar sensitivity as in the pure NOR solution. The analytical performance of the proposed system was validated successfully for pharmaceutical and biological samples with tolerable recovery percentages.

PT Pertamina Hulu Rokan has experienced a decline in net profit, while new work proposals and carry-over (CO) projects have continued to rise. This trend raises concerns about resource management and operational efficiency, requiring an effective solution. This research aims to develop a pre-proposal system using risk-based and workload analysis to prioritize and evaluate Non-Business Development (NBD) investment proposals. The system ensures alignment with strategic objectives and resource capacity. The study introduces a structured pre-proposal process to filter and prioritize proposals at an early stage. The system focuses on critical and feasible projects by applying risk and workload evaluations while reducing redundancies. The research aims to optimize decision-making and align investment proposals with corporate strategies. The methodology includes Business Process Modeling Notation (BPMN) simulations to analyze workflow inefficiencies and Focus Group Discussions (FGD) with stakeholders to refine the system. A trial of the pre-proposal tools for the 2026 Work Plan and Budget (RKAP) demonstrated their effectiveness in managing and prioritizing proposals. The results show that the pre-proposal system reduces incoming proposals from 299 work plans to 232 work plans, a 22.4% decrease. Proposals are categorized into P1 (very high priority) and P2 (high priority) for follow-up, ensuring resources are directed toward impactful projects. This research provides a practical framework for early-stage proposal assessment, enhancing decision-making and investment planning. The system demonstrates the potential to improve efficiency, reduce redundancies, and better align proposals with organizational goals, offering a valuable tool for managing future investment initiatives.

The quiet operation of electric and hybrid vehicles at low speeds can pose a risk to pedestrians. To enhance pedestrian safety, Acoustic Vehicle Alerting Systems (AVAS) have been developed. The objective of this study is to develop an Optical Vehicle Alerting System (OVAS) that is integrated with vehicle speed data, with the aim of improving pedestrian awareness. By processing speed data transmitted via the Controller Area Network (CAN) bus, a variety of light patterns were projected on the road surface using optical sources in daylight conditions. In the pro-posed system, the efficacy of linear laser-based warning patterns generated by laser sources was compared with that of circular light patterns produced by arrays of light-emitting diodes (LEDs) com-bined with lens structures. A vehicle simulation was conducted to measure the light intensity and illumination profiles of laser and LED-based systems in a test environment. The findings revealed that an alerting pattern was generated with an illuminance of 90 lux at a distance of five metres when six LEDs with a wavelength of approximately 505 nm and an output power of 5 mW were employed. Similarly, a dynamic, speed-dependent linear alerting pattern was generated using three laser diodes operating at a wavelength of 532 nm and an output power of approximately 100 mW. The effectiveness of light intensity and illumination profiles was evaluated based on performance at different vehicle speeds. The OVAS system designed with LEDs was mounted at two different heights, 30 cm and 50 cm above the ground, and their performances were compared. It was observed that the OVAS positioned at 50 cm projected a light pattern covering 242% more area at a range of 1-3 metres compared to the system mounted at 30 cm. This emphasises the considerable impact of the installation height on the system's efficacy.

The traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which cause considerable network throughput and bottlenecks at the manager side. All these problems processed using the Agent technology as a solution to distribute the management functionality over the network elements. The proposed system consists of the server agent that is working together with clients agents to monitor the logging (off, on) of the clients computers and which user is working on it. file system watcher mechanism is used to indicate any change in files. The results were presented in real time which is minimizing the cost that represents the important factor to successful management of networks that was achieved using agents.

The major goal of this research manuscript is to conduct economical and feasibility studies of an island photovoltaic/fuel cell (PV/FC) hybrid system. It has been designed to feed electricity to a remote area positioned in albaha zone in Kingdom of Saudi Arabia. To achieve this mission, all system devices are modelled and simulated via optimization mechanisms. An objective function is formulated relied on the total annual cost (TAC). An optimization mechanism, the Flower Pollination Algorithm (FPA) has been applied to obtain the optimum number of the photovoltaic (PV) panels, the electrolyzers; the H2 storage tanks the fuel cells (FCs) and the number of batteries for the proposed system with the least total net present value (TNPV) of the hybrid system. The loss of power supply probability (LPSP) has been utilized to improve the performance of the proposed system. The premier estimation fluctuation effect of the FC, the electrolyzer and the H2 storage tanks on the levelized cost of energy (LCOE) has been applied in the sensitivity test.

The discovery of abundant life in hostile environments, such as within deep rocks, ocean trenches, and hydrothermal vents, has motivated renewed interest in biomolecular adaptation to extreme conditions of temperature, pressure, and chemical makeup. Through an NSF-funded program, established 5 years ago at the Cornell High Energy Synchrotron Source (CHESS), the structural biology beamlines have developed and made available unique sample environments for small-angle X-ray scattering (SAXS) and macromolecular crystallography (MX). The program serves a diverse community of users, as extreme conditions are relevant to many fields of science beyond extremophiles, including biophysics, food science, structural enzymology, drug delivery, and structure-based drug design. I will present a guide to the various techniques available at CHESS through the general proposal system and highlight some of the exciting discoveries these techniques enabled. In addition, I will describe in more detail our HP-MX setup, where crystal structures can be determined with hydrostatic pressure up to 1 GPa (10 kbar) at ambient temperature in diamond anvil cell (DAC). Recent upgrades to the 7B2 beamline have improved achievable data quality and streamlined data collection and processing, making this technique accessible to the wider structural biology community. I will describe the application of HP-MX to structural enzymology, where high pressure conditions reveal conformational changes relevant to allostery and catalysis. Finally, I will offer a preview of techniques under development and future opportunities for scientific collaboration.

Introduction: Today, Internet of Things applications offer new opportunities in all domains like home automation, transportation, medical diagnosis, agriculture, etc. According to McKinsey Global Institute research, IoT will cover a market share of over $11.1 trillion by 2025. Moreover, Semantic web technology approaches are used in IoT applications so that machines can understand and interpret sensor-collected data. Method: Our proposed system uses a DHT11 sensor, NodeMCU for data collection, and ThingSpeak cloud for data analysis and visualization. It utilizes the Protégé tool to develop semantic data modelling using Ontology/RDF graphs and retrieval for future SPARQL queries. Result: This approach ensures the optimal presentation of sensor data and the meaning of data and controls the information for the Home Automation System. By semantic layer, we improved integration, interoperability, discovery, and data analysis. Conclusion: As far as applications are concerned, semantic technologies and IoT sensor data can be transformed into a more valuable and practical format, enabling intelligent applications and systems development across multiple fields, such as smart cities, industrial automation, healthcare, and environmental monitoring.

Background: The increasing demand for electricity, coupled with an imbalanced supply and demand, population growth, and climate change, has prompted the shift from conventional to non-conventional energy systems. However, the unreliability and intermittency of the latter pose a challenge to their feasibility. To address this challenge, a proposal has been made to explore the combination of two renewable energy sources (RES) using a unique DC-DC converter topology, with the aim of meeting the load demand in a sustainable and efficient manner. Objective: The focus of this research was to explore solutions for the challenges associated with operating RES independently, including issues with intermittency, weather dependence, and meeting load demands. The proposed hybrid system features exclusively RES, offering a promising approach to reducing carbon footprint. Ultimately, we aimed to develop a CUK-SEPIC-based converter that can effectively integrate two independent RES to satisfy the load demand of a standalone application. Method: Effective hybrid power generation through RES is a complex challenge, but it has been found that combining solar and biomass energy sources is one of the best options for achieving this goal. To tap into these sources, it is essential to have a suitable power electronic converter, and the CUK-SEPIC converter has been chosen for its many benefits. The features of this converter have been described in detail. The integration of solar and biomass energy sources is achieved using this converter, which has been designed and mathematically modeled in the MATLAB/Simulink environment to ensure optimal performance. To validate the effectiveness of the proposed converter, a comparison with existing power electronic converters has been done using the MATLAB/Simulink platform. Results: The hybrid power generation system model has been comprehensively developed in this work using the sophisticated MATLAB/Simulink environment. The input and output parameters have been diligently estimated through an extensive simulation process. The research has yielded valuable insights, indicating that the CUK-SEPIC converter exhibits an impressive power conversion efficiency of 96.57%, along with an overall step-up ratio of 5.25 and significantly reduced ripple content. Conclusion: Upon conducting a comprehensive analysis of the CUK-SEPIC DC-DC converter, it has been observed that the proposed system exhibits significant promise in rectifying the reliability issues commonly associated with renewable energy power generation. Therefore, it is recommended that this system be considered for implementation in rural electrification initiatives. Furthermore, it is worth noting that this system represents one of the most recent developments in the field of renewable energy power generation technology.

The most important factor in improving the quality of medical services at present is the continuous improvement of all aspects of the activities of a medical organization, which determines the appropriateness of using modern approaches to organizational development in medical institutions with their appropriate adaptation.The paper presents the results of the analysis of 16 known models of organizational development, as well as the performance indicators of the Kaidzen proposal system at 19 Russian and foreign enterprises in various industries. An analysis of the authors’ long-term experience of working with small groups on standardization of treatment and diagnostic processes at the N.V. Sklifosovsky Research Institute for Emergency Medicine of the Moscow Health Department was conducted, which confirmed the assumption that medical personnel of all categories have a large number of ideas for improving the organization’s work and are willing to participate in their implementation.The article presents the author’s model of the system of organizational development of treatment and diagnostic processes of a multidisciplinary hospital, which is based on the concept of personnel orientation and the basic principles developed by the authors.In the part of the model detailing, a developed system of classification features is presented, which allows determining the belonging of a proposal for improvement to a certain group or class, as well as the approach proposed by the authors to sorting proposals at various stages of working with them.The paper presents the results of testing the described approach using the example of proposals for improvement formulated by a pilot group of nurses of the operating unit of the N.V. Sklifosovsky Research Institute for Emergency Medicine of the Moscow Health Department. Conclusions are made on the possibilities of applying the presented research results for medical organizations, as well as promising areas for the development of the presented approach.

This article examines the performance of cooperative NOMA systems in a massive MIMO configuration inside the sixth-generation (6G) network, including scenarios with and without reconfigurable intelligent surfaces (RIS). The emphasis is on comprehending the effects of implementing static, dynamic, and intelligent RIS on the capacity of the cooperative NOMA system with differing user quantities. The study examines how different user loads and RIS densities affect average latency and effective area spectral efficiency (EASE). Integration of a proposal system in a novel way with water-filling in the multi-user channel logarithm to enhance capacity, average latency, and EASE for diverse user densities. The results show that RIS significantly improves capacity, average latency, and EASE, especially with intelligent RIS. All scenarios, in conjunction with the suggested algorithm, markedly improve network performance, particularly under conditions of increased user demand. Additionally, it enhances the system's capability and spectrum efficiency, especially when deployment numbers rise.

In recent years, the power industry has seen a rapid increase in renewable energy, leading to more frequent start-ups and shutdowns of thermal power generation facilities to cover output fluctuations and adjust supply-demand balance. Various types of piping are used in thermal power plants, and during the start-up and shutdown processes, the internal fluid fluctuations are significant, resulting in increased vibrations. Vibrations that were tolerable under conventional operation methods may lead to pipe rupture as the frequency of start-ups and shutdowns increases, posing a concern for stable power supply. Until now, it has been difficult to determine whether countermeasures are necessary, as some pipe vibrations lead to rupture while others do not. Moreover, to suppress pipe vibrations, it is necessary to add fixed supports at appropriate locations. However, identifying the appropriate locations for fixed supports requires vibration measurements and advanced analysis by expert technicians, which demands significant time and cost. To address this, our company has developed a device called the "Automatic Pipe Vibration Suppression Proposal System", which can quickly determine whether the vibrations are problematic and automatically propose the locations for fixed supports to suppress vibrations early and cost-effectively when vibration issues arise. In this paper, we report on the analysis and proposal of countermeasures for actual piping failures caused by resonance phenomena at a thermal power plant. The study combines three approaches: observation of the fractured piping surface, analysis using our automatic proposal system, and examination of operational data.