Developments In Field Research Articles

A review of the progress and developments of green chemistry

The purpose of this review is to present the progress and development of a fundamental field that aims to design chemical processes with as little adverse effect on the environment as possible. The paper discusses the relevance of changes in industries considering the impact of the conventional chemical industry on the environment, people, and resources, with pollution, toxicity, and depletion of the resources being alarming effects. Waste minimization as one of the ten principles of green chemistry, atom economy and safer solvents and auxiliaries, is also described in detail. In the best practice examples drawn from key industries such as Unilever and BASF, a positive approach toward integrating biodegradable materials and renewable resources is evident. Furthermore, new developments in the catalysis and biocatalysts techniques as well as innovative methods of carbon dioxide conversion are also explored to demonstrate that sustainability is a core direction of chemical transformations. Nevertheless, prospects are evident albeit constrained in economic, regulatory, and technical sectors. Overcoming these barriers is crucial to the complete realization of green chemistry in the industry and the scale-back of environmental harm. It is for this reason that the present review offers a final reflection on how both green chemistry and a circular economy can change various sectors and offer more sustainable solutions.

Mapping the evolving trend of research on leukocyte telomere length: a text-mining study

BackgroundSubstantial evidence indicates that measuring leukocyte telomere length (LTL) is a useful tool that may be considered as a valuable biomarker of individual biological age, correlating with numerous chronic disorders. However, to date, there has been a lack of in-depth understanding regarding the current landscape and forthcoming developments in the LTL field. Therefore, this study aimed to utilize bibliometric methods to summarize the knowledge structure, current focus, and emerging directions in this field.MethodScientific publications on LTL spanning the period from 2000 to 2022 were acquired from the Web of Science Core Collection database. Several bibliometric tools including CiteSpace, VOSviewer, and an online website were utilized for bibliometric analysis. The primary evaluations encompassed investigating the major contributors and their collaborative relationships among countries/regions, institutions, and authors, conducting co-citation analyses of authors, journals, as well as reference, examining reference bursts, as well as performing co-occurrence analyses of keywords.ResultsThere are 1818 papers with 66,668 citations identified. Both the annual publication and citation counts on LTL exhibited significant upward trends. The United States emerged as the most prominent contributor, as evidenced by the greatest volume of papers and the highest H-index value. University of California San Francisco and Aviv A were identified as the most productive institution and author in this domain, respectively. Reference analysis revealed that longitudinal study and mendelian randomization study are the most concerned research method in this field recently. Keywords analysis showed that the most concerned diseases in LTL fields were aging, inflammation, cardiovascular diseases, endocrine diseases, neurological and psychiatric diseases, and cancers. In addition, the following research directions such as “COPD”, “mendelian randomization”, “adiposity”, “colorectal cancer”, “National Health and Nutrition Examination Survey (NHNES)”, “telomerase reverse transcriptase”, “pregnancy” have garnered increasing attention in recent times and hold the potential to evolve into research foci in the foreseeable future.ConclusionThis is the first bibliometric study that provides comprehensive overview of LTL research. The findings of this study could become valuable references for investigators to explore and address the current and emerging challenges in LTL research.

Optimization Analysis of Low-Noise Amplifier

This paper mainly analyzes the important component of a wireless receiver – low-noise amplifiers (LNA). Low-noise amplifiers can reduce the effect of noise on the received signal and amplify signals. For the better development of wireless communication technology, remote control, and other related fields, it is necessary to optimize the design of low-noise amplifiers, so that low-noise operational amplifiers can finally obtain high gain, low noise figure, and other advantages. This paper analyzes the optimization design of LNA in the field of Global Navigation Satellite Systems (GNSS). This paper analyzes a low-noise amplifier design with the operation of concurrent dual-band for GNSS receivers with a single channel. The optimized design of a low-noise amplifier in this field uses a low-cost 0.18µm CMOS tube and adds load capacitances and a feedforward path to reduce the noise figure (NF) of the system. Noise figures in 1.2 GHz and 1.57 GHz bands are reduced by about 0.9 dB. In addition, the high gain, high linearity, and other excellent properties are shown in 1.2 GHz and 1.57GHz. In the application of 5G/6G network technology, the optimized design of Gallium Arsenide (GaAs) PHEMT technology with a gate length of 0.3µmm and inductively degraded common source topology phase structure is adopted. From this optimization, the noise figure is optimized to 1.3-1.4, and the gain of the system is 20-22 dB. At the same time, good linearity is also shown. Then, the prospects of semiconductor materials such as Gallium Nitride (GaN) and Indium Phosphide (InP), which have the advantages of high electron mobility and low-cost design, in optimizing low noise operational amplifiers. To a certain extent, the in-depth study of the optimal design of LNA can improve the technical level of wireless communication, aerospace, and other fields.

Extremal Functions and Calderon’s Formulas for the Riemann-Liouville Two-wavelet Transform

The Riemann-Liouville operator has been extensively investigated and his witnessed a remarkable development in numerous fields of harmonic analysis. Knowing the fact of the study of the time-frequency analysis are both theoritically interesting and pratically useful, we investigated several problems for this subject on the setting of the Riemann-Liouville wavelet transform. Firstly, we introduce the notion of Riemann-Liouville two-wavelet and we present generalized version of Parseval’s, Plancherel’s, inversion and Calderon’s reproducing formulas. Next, using the theory of reproducing kernels, we give best estimates and an integral representation of the extremal functions related to the Riemann-Liouville wavelet transform on weighted Sobolev spaces.

Flow Field Analysis and Development of a Prediction Model Based on Deep Learning

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future.

Hüquq texnologiyaları və onların vəkillik sahəsinə inteqrasiyası

Since the end of the last century, the wide spread of digital technologies has had an important impact on the development of many fields. Technological innovations, like in other professional fields, are increasing their impact on the field of advocacy day by day. Some legal technologies reduce lawyers' bureaucratic burdens and perform repetitive tasks, while others help citizens or companies solve simple legal problems without the need for a lawyer. This article examines the concept of Legal Tech, the areas of use of legal technologies, their categories and their application in the field of advocacy, and highlights how lawyers can provide more efficient and better quality legal assistance by using legal technologies in the performance of their profession. The article also touched upon the issues of using artificial intelligence in the legal profession, the practice of applying legal technologies in foreign countries and the level of development of these technologies in our country. Thus, the extensive use of legal technologies by a number of advanced countries of the world, especially the application of technologies based on artificial intelligence in international and national legal systems, has made an important contribution to the development of this field. Keywords: legal technologies, lawyer, electronic advocacy, artificial intelligence, e-government, e-court, e-discovery, technological applications, data security.

Monitoring of Broccoli Flower Head Development in Fields Using Drone Imagery and Deep Learning Methods

For different broccoli materials, it used to be necessary to manually plant in a large area for the investigation of flower ball information, and this method is susceptible to subjective influence, which is not only time-consuming and laborious but may also cause some damage to the broccoli in the process of investigation. Therefore, the rapid and nondestructive monitoring of flower heads is key to acquiring high-throughput phenotypic information on broccoli crops. In this study, we used an unmanned aerial vehicle (UAV) to acquire hundreds of images of field-grown broccoli to evaluate their flower head development rate and sizes during growth. First, YOLOv5 and YOLOv8 were used to complete the position detection and counting statistics at the seedling and heading stages. Then, UNet, PSPNet, DeepLabv3+, and SC-DeepLabv3+ were used to segment the flower heads in the images. The improved SC-DeepLabv3+ model excelled in segmenting flower heads, showing Precision, reconciled mean F1-score, mean intersection over union, and mean pixel accuracy values of 93.66%, 95.24%, 91.47%, and 97.24%, respectively, which were 0.57, 1.12, 1.16, and 1.70 percentage points higher than the respective values achieved with the DeepLabv3+ model. Flower head sizes were predicted on the basis of the pixel value of individual flower heads and ground sampling distance, yielding predictions with an R2 value of 0.67 and root-mean-squared error of 1.81 cm. Therefore, the development rate and sizes of broccoli flower heads during growth were successively estimated and calculated. Compared with the existing technology, it greatly improves work efficiency and can help to obtain timely information on crop growth in the field. Our methodology provides a convenient, fast, and reliable way for investigating field traits in broccoli breeding.

Selection of Nucleotide-Encoded Mass Libraries of Macrocyclic Peptides for Inaccessible Drug Targets.

Technological advances and breakthrough developments in the pharmaceutical field are knocking at the door of the "undruggable" fortress with increasing insistence. Notably, the 21st century has seen the emergence of macrocyclic compounds, among which cyclic peptides are of particular interest. This new class of potential drug candidates occupies the vast chemical space between classic small-molecule drugs and larger protein-based therapeutics, such as antibodies. As research advances toward clinical targets that have long been considered inaccessible, macrocyclic peptides are well-suited to tackle these challenges in a post-rule of 5 pharmaceutical landscape. Facilitating their discovery is an arsenal of high-throughput screening methods that exploit massive randomized libraries of genetically encoded compounds. These techniques benefit from the incorporation of non-natural moieties, such as non- proteinogenic amino acids or stabilizing hydrocarbon staples. Exploiting these features for the strategic architectural design of macrocyclic peptides has the potential to tackle challenging targets such as protein-protein interactions, which have long resisted research efforts. This Review summarizes the basic principles and recent developments of the main high-throughput techniques for the discovery of macrocyclic peptides and focuses on their specific deployment for targeting undruggable space. A particular focus is placed on the development of new design guidelines and principles for the cyclization and structural stabilization of cyclic peptides and the resulting success stories achieved against well-known inaccessible drug targets.

Trend Analysis of Cross-Border Electricity Trading in Pan-European Network

The long-term global target of facilitating energy security and optimizing resource utilization while supporting the transition to sustainable energy systems has led the pathway towards regional cooperation in the context of energy infrastructures and trading. Recent trends highlight the development of transnational energy grids, pipelines, and renewable energy projects, facilitating efficient distribution of electricity, gas, and other energy forms. These advancements offer benefits such as increased reliability of energy supply, cost savings through shared resources, and strengthened relationships between countries. However, challenges persist, including regulatory differences, geopolitical tensions, alignment of market rules as well as substantial investment requirements in infrastructure and technology. Addressing these challenges necessitates harmonized policies, robust legal frameworks, and cooperative international governance, which are crucial for effective cross-border energy trading. The aim of the work is to present the current landscape as well as analyze the latest trends and developments in the dynamic field of electricity interconnectors, providing insights into its trajectory and implications for the European internal energy market.

Multi-objective optimization algorithms for building performance assessment – A benchmark

The increasing development in the computational field that allowed software and hardware advances enables more refined building performance analysis. Simulation-based optimization (SBO) methods allow high standards to be achieved by combining parametric modeling, simulation, and optimization methods. However, SBO methods still need development, especially regarding the correct choice of the optimization algorithm based on the specific characteristics of each problem. This study proposes a multi-objective optimization algorithms benchmark by comparing seven multi-objective optimization algorithms: RBFMOpt, NSGA2, MHACO, NSPSO, MOEA/D, HypE, and SPEA2, across nine building-related problems, including thermal, energy, and daylight simulation. The problems varied from 5 to 18 discrete, continuous, and mixed parameters. The objective functions varied between two and three. We used the hypervolume indicator, IGD+, GD+, and EPS + to compare algorithms’ performance and assess the tendency to reduce computational costs. We also performed the Kruskal-Wallis non-parametric test to analyze the impact of multiple runs on the hypervolume indicator. The results showed that RBFMOpt and HypE perform best across all problems. However, RBFMOpt tends to reduce computational costs since the algorithm requires fewer simulations to obtain the best results.

Application of Two-Dimensional NMR for Quantitative Analysis of Viscosity in Medium–High-Porosity-and-Permeability Sandstones in North China Oilfields

The viscosity of crude oil plays a pivotal role in the exploration and development of oil fields. The predominant reliance on laboratory measurements, which are constrained by manual expertise, represents a significant limitation in terms of efficiency. Two-dimensional nuclear magnetic resonance (NMR) logging offers a number of advantages over traditional methods. It is capable of providing faster measurement rates, as well as insights into fluid properties, which can facilitate timely adjustments in oil and gas development strategies. This study focuses on the loose sandstone reservoirs with high porosity and permeability containing heavy oil in the Huabei oilfield. Two-dimensional nuclear magnetic resonance (NMR) measurements and analyses were conducted on saturated rocks with different-viscosity crude oils and varying oil saturation levels, in both natural and artificial rock samples. This study elucidates the distribution patterns of different-viscosity crude oils within the two-dimensional NMR spectra. Furthermore, the T1 and T2 peak values of the extracted oil signals were employed to establish a model correlating oil viscosity with NMR parameters. Consequently, a criterion for determining oil viscosity based on two-dimensional NMR was formulated, providing a novel approach for estimating oil viscosity. The application of this technique in the BQ well group of the Huabei oilfield region yielded an average relative error of 15% between the actual oil viscosity and the computed results. Furthermore, the consistency between the oil types and the oil discrimination chart confirms the reliability of the method. The final outcomes meet the precision requirements for practical log interpretation and demonstrate the excellent performance of two-dimensional nuclear magnetic resonance (NMR) logging in calculating oil viscosity. The findings of this study have significant implications for subsequent exploration and development endeavors in the research area’s oilfields.

Design of dual-band filtering patch antenna based on SIR multimode resonator

Abstract With the development of the communication field, more and more communication devices need multiple single-frequency antennas to work simultaneously. However, using multiple single-frequency antennas results in larger sizes. The miniaturized and dual-band communication system has become a new development trend. This paper designs a dual-band filtering patch antenna based on a multimode resonator, which is cascaded. The filter resonator part adopts step impedance resonance for branch shunt design, which can flexibly control the center frequency of two frequency bands. The filtering antenna utilizes a dual-layer substrate structure and achieves an integrated design through coupling probe feeding. This reduces the horizontal dimensions and improves miniaturization. Multiple radiation nulls are introduced outside the two-passband, and roll-off characteristics are evident. In addition, this design realizes high selectivity in the operating band. The antenna has large gains of 1.71 dBi and 6.25 dBi. It has good filtering and radiation characteristics.

Social Media as a Village Government Communication Channel in Improving Public Services to the Community in Minahasa Regency

Public service issues are still often the main topic in society, starting from the lack of clear information and slow service that occurs in government in various cases, both related to information, administration and implementation of development in various fields, to the lack of resources that are able to provide the best service to the community. The current era of information technology, new media, which includes social media, is certainly one of the right solutions in overcoming communication and information problems for the government, but the problem is whether social media has been used optimally in carrying out public service duties by the government, especially the government in the village. Along with the many complaints from the public regarding government services, even though communication and information technology is already present in the midst of society and the government. This is the basis of this study, to be studied and studied in more depth, using a qualitative research approach, related to how social media becomes a communication channel for village governments in improving public services. Village governments in Minahasa Regency, North Sulawesi Province have not optimally used social media as an information channel in an effort to improve public services. This is evidenced by the lack of official social media from the village government as a media/information channel in supporting public services. Not all village governments have the human resource capabilities that can manage and operate social media well, actively and intensively in providing information to the public regarding programs, policies, and government regulations. Most village governments in Minahasa Regency do not yet have official social media as a medium/channel of information to the public. There are still many village government officials who use their personal social media for the needs of conveying information to the public, even then only when necessary. Obstacles to the Village Government in utilizing social media to improve public services include limited human resources, technological infrastructure, and cultural constraints, these factors affect the effectiveness of communication through social media.

Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis

Solar cell research aims to improve power conversion efficiency (PCE). This field has an extensive body of literature on the Web of Science. For researchers, it is impossible to understand the development of the entire field comprehensively through traditional reading methods. Knowledge is recorded in the literature by text and numbers. Researchers acquire knowledge through literature surveying, text reading, and thinking. The conversion from text and numbers to knowledge can be automatically completed by machines, which can avoid path‐dependent perspectives. In this work, an intelligent machine learning method for literature structure delineation and information extraction is proposed. As an example, a knowledge base of organic solar cells (OSCs) is extracted including topic analysis of literature, numerical characteristics of performance, and material information. Seven major research directions of OSCs are identified. The correlations between key performance parameters, including PCE, short‐circuit current density (JSC), open‐circuit voltage (VOC), and fill factor (FF), are revealed from text mining. A donor–acceptor material map of PCE is constructed which provides a road map for OSCs, indicating the bottleneck of this field. Moreover, the method of machine intelligence developed here can be used in any other materials field, aiding a comprehensive understanding of the development quickly.

Carbon dioxide injection into the Achimov formations using reinjection technology by the example of Ach3-4 formation in the Novo-Urengoy area of the Urengoy field

The prerequisites for the study are the calculation results for the cycling process, where carbon dioxide is proposed as the injection agent into the Achimov formations instead of dry gas, with the goal of increasing the condensate recovery factor. The work is focused on the efficiency assessment of carbon dioxide reinjection technology and reducing carbon footprint at a late stage of field development. The research object is the Аch3-4 formation within the Novo-Urengoy license area of the Urengoy field. The leading method to identify this problem is the results of the full-scale composite dynamic model in the ECLIPSE 300 format. The model takes into account the history of field development on depletion. The articles deals with two schemes for injecting carbon dioxide into the formation. In the first scheme, pure carbon dioxide is injected in a closed-loop system, but carbon neutrality through storage is not achieved. In the second scheme, carbon dioxide is injected using reinjection technology. Once injection begins, gas production stops. Only the condensate separated from the formation gas during low-temperature separation is sold and sent for further processing. After allocation of the condensate, the mixture of natural gas and carbon dioxide, in a specific proportion, is sent to the compressor station for reinjection into the formation in a gaseous state. Injecting pure carbon dioxide achieves a condensate recovery factor similar to that of gas injection with a 30 % carbon dioxide mixture. However, this option is less economically viable compared to the base and other scenarios due to high capital costs for upgrading the existing gas processing equipment (requiring the construction of an amine treatment unit). With carbon dioxide injection using reinjection technology, in addition to recovering extra condensate that had condensed during natural depletion, a reduction in the carbon footprint is also achieved. To maximize the condensate recovery factor, the optimal concentration of carbon dioxide in the injection mixture has been determined. The optimal timing for the start of injection was identified to maximize gas recovery. Economic efficiency is expected from the additional recovery of condensate trapped in the reservoir and from achieving carbon neutrality through the monetization and storage of carbon dioxide.

Automated methodology for calculating key production indicators of oil field development for business planning model

Background: In order to secure the stable and efficient operations of oil-producing companies, it is essential to forecast the profitability and ROI of the enterprise, as well as to regulate development for the next 5 years. Accurate forecasts facilitate more informed planning and decision-making, directly influencing the economic sustainability and competitiveness of the enterprise. Aim: The purpose of this study is to develop an innovative approach to automate the methodology for calculating key development indicators in a business planning model. Materials and methods: The study utilizes methods for collecting and analyzing production and geological data, empirical forecasting models, and statistical analysis techniques to enchance the accuracy and reliability of forecasts. This approach employs modern algorithms and technologies to process large volumes of data, which allows for more accurate and reasonable forecasts of key production indicators of the field development. Results: This methodology can be applied to forecast a five-year business plan and evaluate its expected implementation. It is integrated into the ‘Production planning and monitoring’ of the ABAI information system, which allows direct data export from the database, automates the monthly monitoring of production indicators, and generate reports for further export. Conclusion: The proposed method for automated planning of key production indicators of the development enhances the accuracy and efficiency of forecasting, thereby improving the quality of planning and evaluating the implementation of the business plan. This contributes to more informed and strategically validated management of oil production processes. Automation of planning processes reduces the labor costs traditionally associated with manual analysis and calculations, freeing up resources for more strategic purposes. This enables rapid responses to changes in production conditions and prompt adjustments of plans. As a result, managers can allocate resources more efficiently, minimize risks, and increase the overall productivity of oil production operations.

Hollow Nanobox‐Shaped Cu2‐xS@ZnxCd1‐xS Heterojunction by Light Multireflection with Z‐Scheme Mechanism for Enhanced Photocatalytic Hydrogen Production

AbstractAchieving efficient spatial photoinduced charge separation and light utilization for improving the high photocatalytic activity is still a major obstacle. Here, a Cu2‐xS@ZnxCd1‐xS (Cu2‐xS@ZnCdS) heterojunction is reported featuring a distinctive core–shell hollow nanobox structure. The Cu2‐xS@ZnxCd1‐xS exhibits high photocatalytic activity in hydrogen evolution reaction (HER) at the rate of 8175 µmol∙g−1∙h−1 under visible light irradiation, outperforming pristine ZnCdS nanoparticles and Cu2‐xS hollow nanoboxes. The remarkable activity and outstanding stability of Cu2‐x@ZnCdS are attributed to the development of robust interfacial electric fields, and improved light multireflection absorption efficiency as well as the Z‐scheme mechanism. The electron paramagnetic resonance (EPR) and in situ X‐ray photoelectron spectroscopy (XPS) measurements gave the direct evidence of the formation of the Z‐scheme, which maintained the high redox potentials of each ZnCdS and Cu2‐xS. This study gives the guideline for the design of heterojunction with the Z‐scheme mechanism, leading to the high photocatalytic performance and remarkable stability.

Experience in developing carbonate formations with hydraulic fracturing at fields of Gazprom Neft Group

The development of carbonate formations with hydraulic fracturing is a very interesting and controversial process. In contrast to terrigenous formations, zonal stimulation can be performed by both acid and proppant fracturing methods, and the abundance of process variations expands the options even further. Although there is a general concept of choosing hydraulic fracturing technology depending on the geological and physical characteristics of the formation, the qualitative choice is complicated by the fact that effective solutions for one reservoir could not show the same efficiency in similar conditions at another one. For this reason, from the point of view of working with carbonates, to have as extensive experience as possible in various well conditions in order to minimize production risks and costs associated with the choice of approach and zonal stimulation technology. The study aims to provide a technological overview of effective fracturing solutions for carbonate formations, seeking to understand their features and applicability depending on well and geological conditions. An important place is given to the communication of experience in the application of stimulation technologies that have proven themselves at carbonate formations of Gazprom Neft and show the risks and limitations that can be encountered when choosing a particular solution. It describes experience in hydraulic and multistage fracturing, technologies, approaches and their features depending on geological conditions of carbonate formations. The study also outlines the actual experience of using hydraulic fracturing technologies at carbonate formations, their comparative effectiveness, and the most successful practices based on the actual experience of the work performed. Much attention is paid to comparing the effectiveness of acid fracturing, acid-proppant fracturing, and variations of fracturing on viscous acid compositions. The findings not only give an idea of the technological diversity of the types of zonal stimulation, but also highlights their comparative effectiveness in geological and physical conditions of the reservoir. The reflected experience can help to choose more effective solutions in the development of similar formations, reduce risks at early stages of preparation for fracturing and helps in deciding on the choice of stimulation technology, thereby improving the quality and efficiency of fields development.

Hybrid Refractive and Diffractive Testing Method for Free-Form Convex Mirror in High-Resolution Remote-Sensing Cameras

The development of high-resolution and large field of view remote-sensing cameras is inextricably linked to the application of free-form mirrors. The free-form mirror offers higher design of freedom and is more effective at correcting aberrations in optical systems. The surface shape error of a free-form mirror directly affects the imaging quality of remote-sensing cameras. Consequently, a high-precision free-form mirror detection method is of paramount importance. For the convex free-form surface mirror with a large aperture, a hybrid refractive and diffractive testing method combining computer-generated holography (CGH) and spherical mirrors for high-precision null test is proposed in this paper. When comparing the effect of error and the detection sensitivity of different designs, the results showed that the influence of the system error is reduced by about 42% and the sensitivity is increased by more than 2.6 times. The proposed method can achieve higher testing accuracy and represents an effective and feasible approach for the surface shape detection method.

The Impact of Supportive Leadership on Work Stress: An Empirical Research in the Ministry of Iraqi Interior

Purpose: The purpose of this research is to discover the effect of supportive leadership on work stress in the Ministry of Iraqi Interior / Human Resources Management to achieve a happy life for its employees and hence achieve the desired level of performance. Theoretical Framework: The current research deals with introducing the Ministry of Iraqi Interior / Human Resources Management to the latest developments in the administration field through supportive leadership style and work stress, as well as presenting the latest concepts, importance, and models that previous researchers have arrived at. It contributes to expanding the researched organization's awareness of adopting any of these models that are appropriate for their goals and future direction. Design/Methodology/Approach: The research analyses administrative data within the Ministry of Iraqi Interior / Human Resources Management (2023-2024), using the dimensions of supportive leadership (empowering employees, fairness of treatment, and inspirational motivation) and the dimensions of work stress (role ambiguity, role conflict, workload, and work nature). It will determine the factors affecting them. Findings: The findings of this current research have approved the key effect hypothesis (supportive leadership style and its dimensions have a significant and negative impact on work stress and its dimensions). Research Implications: The results most leaders of human resources in the under-study organization practice the dimension of fair treatment more than other dimensions (empowering employees, and inspirational motivation. Originality/Value: This research seeks to enhance awareness of the importance of the negative role of a supportive leadership style on work stress. This research has dropped the knowledge gap between them and uses a framework that is compatible and harmonious to save the lives of the employees and at the same time increase organizational performance.