Spectrum of Engineering and Management Sciences
Spectrum of Engineering and Management Sciences (SEMS) is an international, peer-reviewed journal dedicated to publishing high-quality research at the interface of engineering, technology, and management sciences. The journal focuses on studies that advance theoretical understanding, methodological innovation, and practical applications in complex industrial, technological, and organizational contexts. SEMS provides a platform for original research, rigorous analytical studies, and comprehensive reviews that integrate engineering techniques with managerial insights. The journal prioritizes submissions that present novel models, decision-support systems, and data-driven approaches with clear theoretical and practical contributions.
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties and that the Article has not been published elsewhere. Author(s) agree to the terms that the SEMS Journal will have the full right to remove the published article on any misconduct found in the published article.
THE EFFECT OF STIMULANTS ON THE GERMINATION, DEVELOPMENT, AND YIELD OF SOYBEAN CROP
In the context of global climate change, drought, and extreme weather conditions, the effectiveness of applying stimulants to soybean crops after winter wheat has been determined for the effective use of land, growing two harvests per year, meeting the population's demand for food products and livestock for feed, preserving and increasing soil fertility. The most favorabletemperature forintensivegrowth and development of soybeans is around 20-25 0C, however, it can grow well even in the temperature range of 25-30 0C. In the experimental area, the average air temperature in July was 31.2-33.3 0C, the highest air temperature was 42.2-44.8 0C, at such a high temperature, the soybean plant is under stress and the possibilities of growing a high yield are limited. To ensure the intensive growth and development of soybeans and obtain high yields, the use of Uzgumi and Ma'suda stimulants in various doses before sowing seeds and during the growing season was studied. When treating soybean seeds sown repeatedly after winter wheat with the Uzgumi stimulant at a rate of 0.6 l/t and the Ma'suda stimulant at a rate of 3.0 l/t, it accelerated by 4.9-6.8%, and germination was observed 1-2 days earlier. When Uzgumi was applied to soybean seeds at a rate of 0.6 l/t, at 3-5 leaves at a rate of 0.2 l/ha, at budding at a rate of 0.3 l/ha and at flowering at a rate of 0.4 l/ha, the plant height increased by 2.5 cm, the number of pods increased by 2.5 pieces, when Ma'suda stimulant was applied to soybean seeds at a rate of 3.0 l/t,at 3-5 leaves at a rate of 6.0 l/ha and at budding at a rate of 9.0 l/ha, the plant height increased by 1.5 cm, the number of pods increased by 2.2 pieces. When treating repeated soybean crops with Uzgumi and Ma'suda, the dry mass increased by 1.79-2.93 g and more biomass accumulated. When using the stimulants Uzgumi and Ma'suda, the grain yield of soybeans was 2.02-2.11 t/ha, and the grain yield increased by 0.31-0.40 t/ha
Winter wheat, repeated crop, soybean, seedling, germination, stimulator, application rate, timing, growth, development, dry weight, pod, weight of 1000 grains, grain yield
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INFLUENCE OF GUMIMAX GROWTH STIMULATOR ON SUNFLOWER (Helianthusannuus L.) PRODUCTIVITY
In the southern regions of Uzbekistan, after harvesting winter wheat, there is a frost-free period of 130-140 days, during which the positive temperature for plants is 2400-3200°C. The sum of effective temperatures for sunflower crops is 1850°C for early-ripening varieties and 2150°C for mid-ripening varieties. Therefore, it is possible to effectively use the effective temperatures of this period of the season and grow sunflowers. However, in recent years, unfavorable weather conditions, drought, and water scarcity have negatively affected the steady increase in crop yields. In the context of global climate change, drought, and extreme weather conditions, it has been established that the use of stimulants in sunflower crops after winter wheat is highly effective for the efficient use of land, growing two harvests per year, and meeting the population's demand for food products and livestock feed. When creating a scientific basis for improving the agrotechnology of sunflower cultivation after winter wheat, the creation of a scientific theory and the development of theoretical foundations for the technology of applying physiologically active growth regulators before sowing and during the growing season to the plant is relevant. According to the research results, when treating sunflower seeds with the Uzgumi stimulant at a rate of 0.75 l/t-1 before sowing and 0.4 l/ha-1 during the period of 3-4 pairs of leaves, seedling emergence, growth and development accelerated, seed yield was 1.97 t/ha-1, and oil content was 48.6%, compared to the control variant, the seed yield was 0.35 t/ha-1, and the oil content in the seed kernel was 0.31 % increase achieved.
Repeated crop, sunflower, stimulator, seed, 3-4 pairs of leaves, cultivation, seed yield, oil content
INFLUENCE OF OIL CROPS SOWN AFTER WINTER WHEAT ON SOIL FERTILITY AND THE YIELD OF THIN FIBERED COTTON
In the extreme climate and takyr-like low-humus soils of the Surkhandarya region, located in the southernmost desert zone of Uzbekistan, the study of the influence of winter wheat and repeatedly sown oilseeds on soil fertility, wilt of fine-fiber cotton, and cotton yield under shortrotation crop rotation is the most pressing problem of agriculture. As a result of the accumulation of organic substances in winter wheat and various oilseeds sown after it and their mineralization, an increase in soil humus and other nutrients, soil fertility, improvement of the agrophysical and agrochemical state of the soil, and a decrease in the damage caused by wilt, as a result of which the cotton yield of fine-fiber cotton increased by 4.5-5.5 t/ha-1 compared to the control, and the level of profitability of the farm increased by 20-25%.
Winter wheat, short-rotation crop rotation, repeated oilseeds, soybeans, sunflower, peanuts, safflower, sesame, soil moisture, density, water permeability, agrochemical properties, finefiber cotton, yield, wilt disease, etc.
SPECIES COMPOSITION AND BIOECOLOGICAL CHARACTERISTICS OF INSECTS BELONGING TO THE ORDER RECTHOPEDIA, SPREAD IN PASTURE AREAS
The article presents the results of scientific research conducted to study the species composition and bioecological characteristics of insects belonging to the order OrthopterafoundinthepasturesofsouthernandcentralregionsofUzbekistan.According to the research results, 5 species of true grasshoppers belonging to 4 genera (Tettigonia, Decticus, R1atycleis, Semenoviana.) of the (Tettigoniidae) family, 3 species from the superfamilyofcrickets(Grylloidea),2speciesfromthecalfheads(Gryllotalpidae)family, 27 species from the superfamily of grasshoppers (Acridoidea) Purgomorphidae,Pamphagidae, Acrididae families, a total of 37 species of insects belonging to the order Orthoptera species were recorded. Among these, 25 species were classified as ephemeral and ephemeroid, 34 species as semi-shrub ephemerals, and 23 species were associated with shrub and grass pastures. The identified insect species are distributed in various associations of pasture plants, the distribution, reproduction, and development of species were studied, and it was noted that They have a significant impact on the growth, development, and reproductive success of pasture plants
Pasture, plants, ephemerals, ephemeroids, insects, orthopterans, woodpeckers, crickets, locusts, pests
THEORETICAL AND EXPERIMENTAL FOUNDATIONS FOR RESOURCE-EFFICIENT OPTIMIZATION OF SPRINKLER IRRIGATION USING A LOW-PRESSURE DEFLECTOR NOZZLE UNDER ARID CLIMATE CONDITIONS
This study presents the theoretical and experimental foundations for the resource-efficient optimization of sprinkler irrigation using a low-pressure deflector nozzle under arid climate conditions. Particular attention is given to water droplet motion, droplet formation mechanisms, and evaporation losses, considering aerodynamic drag and the influence of external environmental factors. Water droplet motion was described using differential equations based on Newton’s second law, while droplet diameter was evaluated as a function of outlet velocity, nozzle orifice diameter, surface tension, and fluid density. Evaporation and wind drift losses were analyzed as functions of air temperature, relative humidity, wind velocity, and droplet size, allowing assessment of their combined impact on irrigation efficiency.
low-pressure sprinkler irrigation; deflector nozzle; water droplets; evaporation losses; wind drift; irrigation uniformity; Christiansen coefficient; arid climate; resource-efficient irrigation
INFLUENCE OF CROP ROTATION AND FERTILIZERS ON THE GROWTH, DEVELOPMENT, AND YIELD OF MEDIUM-STAPLE COTTON
Crop rotation is a scientifically based crop rotation over the years. In crop rotation, crops are sown sequentially in all fields. The recurrence of the crop rotation period is called crop rotation. When sowing soybeans, fodder peas, triticale+fodder peas on fields freed from winter wheat, and sowing cotton on harvested fields, the proportion of vegetative and generative organs increases due to intensive growth and development. The best predecessors of cotton were winter wheat, repeated soybeans, fodder peas, triticale+fodder peas, which ensured an additional yield of 0.16-0.22 t/ha-1 of cotton. When sowing green manure crops (soybeans, fodder peas, triticale, triticale+fodder peas) after winter wheat, an additional yield of 0.40-0.56 t/ha-1 or 13.0-18.7% was obtained compared to the control. When sowing cotton after green manure crops, the green mass of intermediate crops was more effective compared to the harvested field, and an additional 0.41-0.56 t/ha-1 of raw cotton was obtained compared to the control variants.
Crop rotation, green manure, intermediate crops, cotton, height, number of yield elements, bolls, opened ones, cotton yield, predecessor crops, soybeans, fodder peas, triticale, triticale+fodder peas, etc.
SYNTHESIS AND INVESTIGATION OF COORDINATION COMPOUNDSOF Co(II) ION WITH 2-HYDROXY-1-NAPHTHALDEHYDE AND 1,2-PHENYLENEDIAMINE
A new metal complex compound with the composition [Co(2-hnaph)2(1,2-phen)2]∙Cl-∙C2H5OH was synthesized by the interaction of 2-hnaph(2-hydroxy-1-naphthaldehyde), 1,2-phen (1,2-phenylenediamine),and CoCl2∙6H2O in ethyl alcohol solutions. The composition, molecular and crystal structures of this metal complex compound were investigated using differential scanning calorimetry (DSC), IR spectroscopy, and X-ray diffraction analysis (XRD). DSC analysis revealed that the thermal decomposition of the organic component in the complex varies, beginning at 75°C and ending in the temperature range of 370°C. A reaction occurs between the oxygen ofthe carbonyl group in the aldehyde form of 2-hnaph and the amino (NH2) groups of 1,2-phen, resulting in the release of one water molecule (>C=O +-NH2→ >C=N-+ H2O). Analysis of two-dimensional fingerprint region plots revealed that H...O/O...H, H...H, and H...C/C...H interactions make the greatest contribution to the Hirshfeld surface. In the complex compound, H...H interactions account for 47.2%, H...C/C...H for 34.4%, and H...Cl/Cl...H for 9.2%, which is the expected state for molecules dominated by oxygen atoms. The complex compound contains H...H (47.2%), H...C/C...H (34.4%), and H...Cl/Cl...H (9.2%),which is the expected state for molecules dominated by oxygen atoms.
2-hydroxy-1-naphthaldehyde, 1,2-phenylenediamine, cobalt (II) chloride hexahydrate, alcohol, complex, ligand, Hirshfeld.
OPTIMIZING THE SWELLING DEGREE OF RUBBER AND ENHANCING ITS OPERATIONAL PROPERTIES USING CELLULOSE FIBERS DERIVED FROM WILD BARLEY STRAW
This study investigates the optimization of the swelling degree of rubber compositions and the enhancement of their operational properties through the strategic incorporation of cellulose fibers derived from wild barley (Hordeumspontaneum) straw. In modern polymer science, replacing synthetic additives with environmentally sustainable fillers from local sources holdssignificant ecological and economic importance. This research evaluates the synergistic effect of cellulose obtained from wild barley when introduced into elastomer matrices along with technical-grade carboxymethylcellulose (CMC). The experimental methodology involved isolating cellulose through the chemical treatment of agricultural waste and systematically incorporating it into the rubber mixture at various concentrations. It has been conclusively demonstrated that these natural cellulose fibers functionas both a reinforcing filler and a structural modifier. The primary focus of the research was on the material's performance in aggressive hydrocarbon environments. The results revealed a significant reduction in the degree of swelling when exposed to oil and gasoline, which in turn indicates a higher cross-link density between the fiber and the polymer matrix, as well as improved interfacial adhesion. Furthermore, mechanical tests demonstrated a considerable increase in tensile strength and hardness, resulting in a nearly twofold improvement in the operational durability of the resulting technical rubber products. The cellulose fibers reinforce the elastomer structure, reduce molecular mobility, and thereby prevent its premature degradation. This proposed technology not only paves the way for the creation of high-value-added industrial products but also promotes the principle of "green chemistry" by converting local plant waste into high-performance materials for the automotive and industrial sectors.
Wild barley straw, cellulose, carboxymethylcellulose, elastomer, rubber, degree of swelling, mechanical strength, modifier, service life, filler.
IMPROVING THE OPERATIONAL PROPERTIES AND CHEMICAL STABILITY OF POLYETHYLENE GEOMEMBRANES FILLED WITH SHEROBOD ARGILLITE FOR AGGRESSIVE SALINE SOIL CONDITIONS
This study investigates the stability of nanocompositegeomembranes based on high-density polyethylene,modified with local argillite minerals from the Sherabad deposit, under conditions of aggressive saline soil. The primary objective of the research is to enhance the chemical degradation resistance and the physical-mechanical properties of the polymer material using nanofillers. Nanocomposite samples were prepared by incorporating various amounts (1-7 wt. %) of argillite into the high-density polyethylene melt via extrusion. The chemical stability of the materials was tested under long-term exposure (90 days) to highly mineralized (sulfate and chloride) aggressive environments. Scanning electron microscopy (SEM) analysis showed a uniform distribution of argillite particles within the polymer matrix and the formation of a tortuous path effect. The results indicate that the addition of an optimal amount of argillite, specifically 5%, increased the tensile strength of the geomembrane by 23% and significantly reduced the diffusion rate of salt ions.Thesefindings provide a scientific basis for the potential of creating highly durable and effective geomembranes for hydrotechnical and land reclamation structures using local mineral raw materials.
HDPE nanocomposites, clay-polymer interfacial interaction, tortuosity coefficient, environmental stress crack resistance, thermomechanical properties, mineral fillers, long-term durability in saline environments.
PlatformGovernance as Decision Support: A Governance RiskIndex for Twitter/X During the 2024 U.S. Presidential Campaign
Online platforms have emerged as essential infrastructure for democratic deliberation, crisis communication, and public healthmessaging.Nonetheless, governance of these spaces remains largely retrospective and principle-based, without concrete evaluative criteria.This article operationalizes six key principle-based elements of platform governance into concrete clause components with quantifiable triggers,to support proactive application. We develop a composite Governance RiskIndex (GRI), integrating empirically defined decision thresholds for four risk components: dispersion,drift, inequality, and toxicity. We estimate toxicity levels from a stratified subsample of the dataset labeled using the Perspective API, aggregated to the daily level. Our results show that toxicity is positively correlated with engagement (r= 0.52,p< .001), as replicated in our data (r= 0.49,p< .001),in line with algorithmic amplification dynamics thatplatform governance must account for. We further find that decision thresholds differ meaningfully between clause components and risk components. A contextual benchmark against 2015–2016 multi-platform baselines reveals that the 2024 Twitter/X environment exhibits substantially different statistical properties in engagement dispersion and toxicity prevalence, highlighting the need for context-dependent calibration of platform governance thresholds. Over a 30-day out-of-sample holdout period, validated exclusively against independently verified external events, the GRI classified governance-event days under retrospective validation with 85.1% accuracy. Under a supplementary labeling scheme that incorporates platform-internal anomaly criteria, accuracy reaches 90.0%, representing an improvement of about 13 percentage points over single-metric baselines, highlighting the advantages of multivariate, multi-faceted operationalization in proactive platform governance.
Leveraging Artificial Intelligence to Study and Forecast its Impactson Employment Management and Wages in China’s Key Cities
The rapid development of artificial intelligence (AI) is transforming global economies, threatening job security across diverse fields, including AI development itself, as automation increasingly handles complex tasks traditionally performed by skilled professionals. As China leads global manufacturing and technological innovation, understanding the labor market implications of AI adoption is critical for shapingmanagement sciencepolicy and workforce development. This study investigates the impact of AI,measured by industrial robot installation density, on employment scale and wages in the manufacturing sectors of Beijing and Shanghai from 2006 to 2020, extending the analysis to 2030 using deep learning. Employing panel data and a long short-term memory (LSTM) neural network, AI’s effects on job displacement and wage growthareanalyzed. Historical findings reveal a nuanced effect: AI adoption is strongly negatively correlated with employment scale (r=−0.90,p< 0.01), with Beijing and Shanghai experiencing employment declines of 44.6% and 40.3%, respectively, from peak levels. Conversely, AI is positively correlated with wages (r= 0.96,p<0.01), with wage increases of 361% in Beijing and 324% in Shanghai. City-specific analyses show Shanghai’s steeper AI adoption correlates with greater employment declines but higher wage growth compared to Beijing. LSTM forecasts predict continued employment declines (49.8% in Beijing, 50.2% in Shanghai by 2030) and wage growth (51.3% in Beijing, 54.6% in Shanghai).Graphs illustrate historical and forecastedmanagementtrends.Country-level synthesis underscores implications for China’s labor market and other developing economies.
State-of-the-Art Comparison of Sensors in Industry 4.0, Industry 5.0,and Low-Cost Monitoring Technologies
Industry 4.0 has transformed modern manufacturing by incorporating internet-connected devices, artificial intelligence, cyber-physical platforms,and advanced data analysis. These innovations have raised automation levels,improved operational performance,and enabled predictive maintenance.Industry 5.0 builds on these foundations by emphasizing collaboration between humans and intelligent machines, promoting environmentally responsible practices, and supporting personalized production models.In parallel,affordable sensor technologies are gaining traction due to their low cost and practical ability to support essential monitoring across diverse applications.This study offers a comparative evaluation of sensor technologies in Industry 4.0, Industry 5.0, and budget-conscious systems,outlining their advantages, limitations, and areas of application.The findings suggest that sensor integration remains central to industrial progress,particularly when linked with IoT, artificial intelligence, and edge-computing solutions that enhance adaptability and real-time decision-making.
Analysing Rice Productivity in Indiausing a Fuzzy Logic Modelbased on Soil Quality Parameters
This study integrates fuzzy logic modelling to evaluate the nonlinear interactions of soil quality parameters(i.e.,soil pH, organic matter, moisture,and nutrient levels)on rice productivity across diverse agro-climatic regions of India, offering improved accuracy over conventional yield prediction models. Membership functionsaredeveloped using empirical field data and expert knowledge. The fuzzy inference system, including fuzzification and rule bases,isimplemented using MATLAB. Fuzzy surface analyses identify optimal productivity ranges, particularly at pH levels of 5.0–7.5 and organic matter between 0.5% and 2.5%, supporting the application of sustainable soil management practices. Additional surface analyses involving interactions (such as soil pH–moistureand nutrient–moisture) confirmthe model’s validity, with predicted yields aligning closely with actual agricultural data.Sensitivity analysis indicatesthat soil pH and nutrient levels havethe greatest impact on rice yields, while organic matter hasmore influence when considered in combination with other factors. The model identifies critical soil factors and provides practical guidance for applying sustainable interventions (such as organic fertilization, efficient irrigation, and crop rotation). It serves as a decision-support tool for policymakers, agricultural planners, and farmers, enabling data-driven, ecologically sound strategies to improve rice productivity and support long-term food security.
The Interdisciplinary Impact of the Quantification of Everything Method
The Quantification of Everything(Q.E.)methodology is a three-step process designed to transform theoretical concepts into mathematical representations and validate the resulting models through the principle of multiple axiomatics. This methodology integrates algorithms and computational techniques, often involvingmanyestimations. Its application spans various scientific disciplines; in this context,its use in economics has been illustrated. A distinctive feature of the Q.E. methodology is its ability to convert empirical theoretical concepts into tractable mathematical forms by utilizing virtual quantity data. This function is especially useful in domains such as the social sciences, where empirical data may be limited or entirely unavailable. By enabling the generation of virtual datasets to test theoretical models, the methodology offers a structured approach for translating qualitative insights into quantitative terms and deriving corresponding mathematical expressions.The Q.E. methodology is a robust and adaptable framework applicable across all epistemic domains.
Uncertainty Quantﬁcatonin Steady-State Heat Transfer: A Comprehensive Analysis of DRAM and MCMC Methods with Applications to Thermal Systems
Thisresearchaddressesthelimitatonsoftraditonaldeterministcmethodsin capturinguncertaintesinheattransfersystems,partcularlyinparameterest-matonanduncertaintyquantﬁcaton.WeaimtoevaluateandcompareDelayed RejectonAdaptveMetropolis(DRAM)andMarkovChainMonteCarlo(MCMC)methodsforuncertaintyquantﬁcatoninsteady-stateheattransfer,usingexper-imentaldatafromacopperrodwith15temperaturemeasurements.Thestudy estmatesheatﬂuxandconvectveheattransfercoeﬃcientparameters,compar-ingresultswithOrdinaryLeastSquares(OLS)estmaton.ResultsshowDRAM producestghterparameterdistributons(0.2312)comparedtoMCMC(0.2641),whilebothmethodsyieldsimilarmeanestmatesanddemonstratestrongnega-tvecorrelatonbetweenparameters.AcomparisonwithOLSshowscloseagree-mentacrossallthreemethods,concludingthatDRAMprovidesslightlysuperior performanceinparameterestmatonaccuracywhileallmethodseﬀectvelycap-tureparameteruncertaintesinsteady-stateheattransferanalysis.
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Bibliometric Analysis: Comprehensive Insightsinto Tools, Techniques,Applications,and Solutions for Research Excellence
Bibliometric analysis has emerged as a vital methodology in understanding research landscapes through the application of statistical and quantitative techniques to academic literature. This study examines the evolution,significance, and application of bibliometric methods, highlighting their role in identifying influential authors, mapping collaboration networks, and uncovering emerging research trends. The analysis underscores the development of bibliometric tools, such as VOSviewer,CiteSpace, and Bibliometrix, and their diverse capabilities in visualizing and analyzing large datasets. By comparing bibliometric analysis with traditional review methodologies like systematic literature reviews and meta-analyses, this work illustrates its efficiency in providing comprehensive insights into research domains while emphasizing its complementary role in a holistic research approach. Through the lens of key challenges and advancements in bibliometric tools and methodologies, this research highlights its indispensable role in shaping academic, policy, andinstitutional strategies in a data-driven era.
AnImplementation of the Entropy Method for Determining Weighing Coefficientsin a Multicriteria Optimization of Public Procurements
Determining the weights of criteria is a key problem in multicriteria analysis models. The problem of choosing an appropriate methodof determining criteria weights in problems of multicriteria decision-making (MCDM) is a very important stage. Most authors suggest classifyingthe models of determining the weights of criteria into subjective and objective models. This paper presents the application of the Entropy methodfor determining criteria weights. The Entropy method is tested in the MCDM problem in public procurements. The results show that the Entropy methodshouldn't be used as a correction method because in most cases, the decision maker Also shows that applying the Entropy method in MCDM problems for determining weighing coefficients can be counterproductive.
A Review and Comparative Study of Cloud Computing and the Internet of Things
The Internet of Things (IoT) and cloud computing aretwo distinct ideas that are impacting our lives in various ways and will continue to be utilized and emphasized in the future of the Internet. These two ideas are combined and compared in this study. IoT and cloud computing havereceived independent attention in numerous research. However, these studies do not go deep enough into its combination which gives rise to newproblems. IoT is experiencing increasing development as the next Internet evolution. IoT allows billions of devices to interact with each other and assign more value to our daily lives. In this paper, we present a literaturereview of cloud computing and IoT. First, we examine the fundamentals of cloud computing and IoT. Then, we talk about how they complement one another and what are their main differences. IoT can greatly benefit from the abundance of resources found in the cloud.
Evaluation and Analysis of Factors Affecting Delaysin Large-Scale Complex Projects: CaseStudy of Oil Well Drilling
The expansion of project scale typically leads to increased complexity,resulting in heightened uncertainty and challenges in predicting issues,evolving conditions, and unexpected opportunities that emerge and are recognized post-initiation. Economic development programs in a country allocate substantial financial resources to oil and energy projects to enhance social welfare and quality of life. Considering the value of these resources and the investment of time, executed projects must minimize delays to avoid the wastage of national capital and allocate resources effectively to enhance the country's economic prosperity. This study examines the drilling of oil wells as a megaproject, conducting research and investigation into this large-scale complex endeavour. According to expert opinions, researcher experience, and prior studies, three hypothetical factors are identifiedas the most significant contributors to project delays. The sample populationsize was determined to be 33 individuals through a comprehensive survey, andthe preliminary study results were employed using Cochran's formula and Morgan's table. A questionnaire comprising 49 questions was subsequently developed and formulated in accordance with the specified hypotheses. The questionnaire was randomly distributed and collected from the samplepopulation following verification of its validity and reliability. Statistical inference methods were utilized to examine and evaluate the hypotheses through the application of Student’s t-test. The results of the three hypothesesare presented according to their significance, ranked from the most influential factor (highest effect)to the least influential factor (lowest effect) on project duration. The three factors are summarized as follows: 1) performance of contractors, 2)performance of domestic producers, and 3) performance ofproject owners.
Marketing Management and Online User Behavior: Future Research
Over the past few years, digital technologies have exerted widespread influence on the business models, products, services,market structures, and consumer behavior of companies. This phenomenon has significantly enhanced the rejuvenation of relationships among companies, markets, and consumers, consequently impacting various marketing mix components.However, with today’s fiercely competitive digital landscape, understanding the evolution of research on digital marketing management and online users’behavior, most influential studies as well as its interrelation with disciplines can provide valuable insights for e-business. Therefore, this study considered conducting a comprehensive review using a bibliometric technique for 282 high-quality papers collected from the Web of Science. The study uncovered that "user acceptance, user-generated content, quality and satisfaction, and planned behavior" received little attention. Thus, future research can examine them.
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Influenceof Artificial Intelligence on Mechanical Engineering Innovation
The integration of artificial intelligence (AI) intomechanical engineering has precipitated a profound transformation in the way engineers conceive,design, and execute projects. This paper explores themultifaceted impact of AI on mechanical engineering innovation, elucidatingthe myriad ways in which intelligent machines are revolutionizing traditional practices and catalyzing unprecedented advancements. In the realm of design, AI algorithms are revolutionizing the conceptualization and optimization processes. By leveraging machine learning and optimization techniques,engineers can explore vast design spaces with unparalleled efficiency,uncovering innovative solutions that might otherwiseremain elusive. These AI-driven design tools not only expedite the development cycle but also enable the creation of products and systems with enhanced performance characteristics, such as improved energy efficiency, structural integrity, and functional versatility. Moreover, AI's influence extends beyond the design phase and permeates the entire manufacturing ecosystem. AI-driven automation is reshaping production lines, enabling agile and adaptive manufacturing processes that respond dynamically to changing demands and conditions. Through the integration of sensors, actuators, and AI-powered control systems, factories are becoming increasingly intelligent and autonomous, optimizing resource utilization, minimizing waste, and maximizing throughput.
Assessing the Problems Encountered Fuzzy Methodology: A Case of Giresun Province
Manufacturing companies are always competing becauseof globalization,both in terms of sectoral level and meeting customer demands. Companies that want to gain competitive advantages want to offer the best quality products and services at the most affordable cost andaim to deliver them to the end consumer by creating added value. This requirement and flexibility level can only be achieved through logistics applications. Businesses that provide efficiency and speed in logistics applications take a few steps forward in market conditions and respond to customer needs ontime. It has become a necessity to use electronic-based systems in logistics applications, especially to meet customer requirements. Information communication-based electronic business has also changed the methods andmodels of doing business for its users. Internet technology has causedbusinesses to change their business models and practices, along with their production and service structures. Developments in e-commerce and technologyhave brought e-logistics applications to the forefront and made it necessary to emphasize them. Because e-logistics applications are importantfor facilitating business operations and increasing performance and customer satisfaction, they also eliminate distances in world trade and offer access tomarkets that are difficult to access. There are various problems in e-logistics applications and it is important to identify them. In this context, the problems faced by businesses using e-logistics become critical and oneof the components that need to be meticulously emphasized. According to the results of the study, it is understood that the most important e-logistics barriers in enterprises are "lack of skill to manage digital technologies to support the system" and "need for qualified personnel". It is obvious that the results will guide business managers.
Interpreting the Determinantsof Sensitivity in MCDM Methods with a New Perspective: Selection with the PROBID Method
It is not a desirable situation when input parametersexcessively affect the results of a system as well as imply unwarranted driftand inefficiency. This situation, which expresses dependence or sensitivityto inputs, is also considered a problem in the multi-criteria decision-making (MCDM)methodology family, which has more than 200 members.A newly produced MCDM method is first subjected to sensitivity tests.MCDM methods are generally evaluated for their sensitivity to weighting methods. Sensitivity is affected by many different parameters such as data, normalization,fundamental equation, and distance type. The commonmethodical approach for sensitivity analysis is to check whether the bestalternative changes with the alteration of weight coefficients. It is problematic to identify sensitivity only in the situation where the ranking position of the best alternative changes. In this study, the sensitivity of the entire ranking is based on a holistic view. Moreover, in the classical method, there is no reference point for sensitivity. Each different MCDM result is comparedto each other and it is claimed that the method that produces rankings that are significantly different from the others is poor. We reinterpret sensitivity using the relationship between dynamic MCDM-based performance and static price towards the selection of an environmentally friendly,traffic-saving performance electric scooter. Two PROBID variants as well as the CODAS method are used in this study to deepen the accuracyin the comparison.Additionally, how four types of weighting methods and six types of normalization types affected MCDM sensitivity is measured with a different statistical framework. The finding from a total of 72different MCDM rankings is striking: If the sensitivity of an MCDM method isgenerally high, the correlation between that MCDM method and the externalanchor (price) is low. Conversely, if sentiment is low, a high correlation with price results.These matching patterns are a unique discovery of this work.
Green Supplier A Comprehensive Review of Recent Studies
This study provides an extensive analysis of current research on the crucial topic of choosing environmentally friendly suppliers by using multi-criteria decision-making (MCDM) methods. The assessment andchoice of environmentally conscious suppliers have become essential in modern business operations due to the growing worries about the environment throughout the world and the necessity of sustainablesupply chain management. This study provides an in-depth analysisof a wide range of MCDM techniques used in green supplier selection, highlighting the advantages, disadvantages, and new directions of each approach. Through the synthesis and analysis of the results of multiple recent studies, this review advances our understanding of the changing environment surrounding green supplier selection and offers useful information to practitioners in the field as well as scholars. In the context of green supplier selection, it emphasizes the necessity of a nuanced and context-specific approach to MCDM, eventually developing environmentally conscious and sustainableprocurement methods that are in line with the changing demands of today's corporate environment.
Ranking of Autonomous Alternatives for the Realization of Intralogistics using the TOPSIS Method
The requirement for intralogistics activities to be automated has been impacted by current trends based on the impact of Industry 4.0, the growth of e-commerce, the emergence of the consumer society, therise in demand for logistics services, etc. Automatization of warehouseintralogistics is fundamental in aiming quickly responds to all userrequests. The most common intralogistics equipment in warehouses is theforklift. However, its engagement results in a low level of automation. Consequently, it is useful to implement autonomous technology, such as automatedguided vehicles (AGVs), automated mobile robots (AMRs), and drones,to have sustainable intralogistics activities. In addition to advantagesand limitations, their application in practice increases performance, adaptability, system efficiency,customer satisfaction, and accuracy and contributes tothe efficiency of the entire supply chain. The operating environment is morehumane,environmental standards are respected, and certain economic advantages are achieved. These technologies are compared using eightcriteria and the technique for order of preference by similarity to ideal solution (TOPSIS)method. AMR was selected as the most suitable option for implementing intralogistics activities. As the automation of intralogistics activities affects the entire supply chain, AMR is the solution that satisfies the social,environmental, and economic requirements of sustainable supply chain management.
MCDM-based Frameworks for Proactively Managing the Most Critical Risk Factors for Transport Accidents: A Case Study in Libya
Road transport accidents are a major public health concern in Libya, with one of the highest death rates in the world. In this paper, we propose a hybrid approach using the improved fuzzy step-wise weight assessment ratio analysis (IMF-SWARA) method to identify the most critical risk factors for transport accidents, and the combined compromise solution (CoCoSo)method to rank the strategies for mitigating these risks. Our study uses a case study in Libya to demonstrate the effectiveness of the proposed approach. The IMF-SWARA method is used to weight the risk factors for transport accidents,and our analysis shows that driver-related factors are the most critical risk factors. We then use the CoCoSo method to rank the strategies for mitigating these risks, and our analysis shows that driver training and education is the most important strategy. Our study suggests six strategies in total, which can be used by policymakers to proactively manage the risks associated with transport accidents in Libya. The proposed approach can be applied to other countries to identify critical risk factors and develop effective risk management frameworks.
Application of Multi-Criteria Decision-Making Methods inWarehouse: A Brief Review
This review paper investigates the application of various multi-criteria decision-making (MCDM) methods in the context of warehouse management, considering papers published from 2010 to date. Warehouses often face many challenges, including efficient inventory management,space optimization, proper resource allocation, and optimal supplier selection. This paper focuses on several well-known MCDM methods that are often used in the context of warehouse management. Full consistency method (FUCOM), analytic hierarchy process (AHP), thetechnique for order of preference by similarity to ideal solution (TOPSIS), weighted aggregated sum product assessment (WASPAS), criteria importance through intercriteria correlation (CRITIC), measurement ofalternatives and ranking according to the compromise solution (MARCOS), best worst method (BMW), evaluation based on distance from average solution (EDAS), correlation coefficient and the standard deviation (CCSD),indifference threshold-based attribute ratio analysis (ITARA), and simple additive weighting (SAW) are some of the methods reviewed in the paper,as well as certain fuzzy versions of the methods. This review paper provides a brief comprehensive overview of the application ofthese methods in the context of warehouse management. Data collection leads to results that tell us that the methods are mainly used in solving problems during the selection of warehouse location, the selection of warehouse equipment, and also in the management of the warehouse itself and the performance of its management. It has also been seen that the methodsare useful even in "green" logistics, as well as in inventory management.
Warehouse; MCDM; Warehouse Location; Warehouse Management
MCDM Methods for Selection of Handling Equipment inLogistics:A Brief Review
This review considers the application of multiple-criteria decision-making (MCDM) methods in the evaluation process of handlingequipment in logistics.In order to present a current review of research papers, 21articles published in the period from 2010 to date were analyzed. All research papers were searched using the Google Scholar web search engine. This paper presents various MCDM methods used to select equipment such as forklifts, cranes,conveyors, automatically guided vehicles, autonomousmobile robots, and reach stackers in warehouses, ports, city logistics, as well as intermodal transport. We analyzed research papers related to the application of MCDM methods including AHP, MABAC, SWARA, ARAS, FUCOM, CRITIC, and MARCOS, as well as various fuzzy methods. As the studies analyzed in the paper have been published from 2010 to the present,this indicates the importance and relevance of this area of research. Thepurpose of this paper is to encourage additional research in this area and provide a summary of the most recent studies in this field. The results of thepaper showed that the most used handling equipment was the conveyor.
Decision-Making for the Bakery Product Transportation usingLinear Programming
This research paper presents a case study of a food chain in Pakistan that aims to transport its bakery products from the manufacturing unit in Islamabad to remote northern regions of the country while minimizingthe total cost of distribution and transportation. The study employs various analytical methods, including linear programming, network optimization, and simulation modelling, to identify the optimal transportation solution that considers geographical barriers, infrastructural limitations, seasonal fluctuations, and regulatory constraints. The researchdelves into the underlying factors that contribute to the complexity and challenges of delivering goods to remote areas and explores the potential benefits of adopting a more holistic and integrated approach totransportation management. The study provides valuable insights and actionable recommendations for the Pakistani food chain and contributes to the broader field of transportation and logistics management.
Transportation Problem; Decision-Making;Operational Research Model; Optimal Solution; Various Analytical Methods