Science and Technology of Engineering, Chemistry and Environmental Protection
ISSN 2959-6157 (Print) ISSN 2960-1339 (Online) Editor-in-chief: Vinod A. Thale （Zhejiang University） Associate Editor: Syed Murawat Abbas Naqvi （Beijing Institute of Technology） Editorial Board Member: Faisal Rafique （Southern University of Science and Technology） Jian Hu （University of Kent） The Journal of Science and Technology of Engineering, Chemistry and Environmental Protection (JSTECEP) is a multidisciplinary, peer-reviewed academic publication dedicated to fostering the next generation of scientific leaders. By providing a professional platform for college students, young researchers, and early-career innovators, JSTECEP bridges the gap between classroom learning and professional academic publishing. Our mission is to democratize the exchange of scientific knowledge. We aim to support emerging scholars by providing a rigorous yet constructive peer-review process that highlights original research, innovative technical solutions, and sustainable developmental practices. Scope of Subjects The journal welcomes submissions that explore the intersections of theory and application across three primary pillars: Engineering: Advancements in mechanical, civil, electrical, and chemical systems, with an emphasis on modern technological integration. Chemistry: Research spanning organic, inorganic, and analytical chemistry, as well as biochemistry and material sciences. Environmental Protection: Innovative approaches to sustainability, climate mitigation, pollution control, and the preservation of natural resources. Why Publish With Us? Support for Emerging Voices: We prioritize high-quality work from students and young professionals who are often underrepresented in legacy journals. Rigorous Peer Review: Every article undergoes a thorough evaluation to ensure technical accuracy and academic integrity. Interdisciplinary Reach: Our broad scope encourages authors to submit work that solves complex, real-world problems involving multiple scientific disciplines. Commitment to Innovation: We celebrate "outside-the-box" thinking and technical creativity that addresses the environmental and industrial challenges of the 21st century. All articles will be submitted to: Google Scholar, Crossref, U.S. Librarian Database, ProQuest (part of Clarivate’s Web of Science), Ulrich's Periodicals Directory, SEMANTIC SCHOLAR, OpenAlex.
Research on the Application of Deep Reinforcement Learning in Logistics Scheduling
Traditionally, the approaches of logistics scheduling are hard to adapt to the rapid changes of the operational conditions and many of them do not have self-adaptive optimization capability, which will leads to the unsmooths and high operational expenses. In this research, we use Deep Reinforcement Learning (DRL) to improve the logistics scheduling. We take the warehouse order picking as an application example, we build the simulated warehouse environment on a grid system and design the intelligent scheduling based on Proximal Policy Optimization (PPO) algorithm. We compare the performance of it with the usual A* path planning method. The results show that the DRL model built on PPO has better results than the A* algorithm for several important measures, for example, the average steps to finish, the rate of order finish and the total reward accumulation. This investigation proves the possibility of DRL to make flexible and smart logistics scheduling, which is both theoretical reference and .a low-cost simulation tool for the intelligent transformation of logistics enterprises.
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The Rossby Number: How Rotation Affects Ocean Circulation
This project investigates how the Rossby number influences ocean circulation. The Rossby number, defined as Ro =U / ( fL) , compares inertial forces to Coriolis forces and determines whether rotation dominates a given flow. Through non-dimensionalization of the Navier-Stokes equations, Ro emerges naturally as the ratio of these two forces. The same scale analysis also yields the Ekman number E =ν / ( fL2 ) , which measures the importance of viscous effects relative to rotation. We systematically define Ro and examine three regimes: Ro <<1 , Ro ~1 , and Ro >>1 . Each regime corresponds to different ocean circulation behaviors, from rotation-dominated large-scale currents to rotation-negligible small-scale motions. A typical mid-latitude ocean current yields Ro ~ 0.001 , confirming that rotation is the dominant control in large-scale ocean dynamics.
How does a low-temperature environment affect the discharge capacity and internal resistance of lithium-ion batteries
The study investigated the performance degradation of 18650 lithium-ion batteries under low-temperature environment. A Arduino-based testing system was used to conduct comparative discharge experiments at 25℃ and approximately -10℃. The system detected and collected the real-time current, voltage and temperature data, and the discharge capacity was calculated by current integration. The results of this study reveal that low-temperature environment will greatly increase internal resistance, lower the discharge plateau and reduce capacity of a lithium-ion battery. The root cause lies in higher electrolyte viscosity and slow lithium-ion migration. The article summarizes existing optimization methods, analyzes experimental limitations, and provides practical improvement strategies for batteries used in electric vehicles and portable electronics.
Evaluate the extent to which movement inspired by biological organisms enhances overall robotic development in unstructured environments
This article evaluates the extent to which movement inspired by biological organisms enhances robotic development in unstructured environments. It examines how biological principles influence robotic mobility, stability, sensing, energy use, and practical adaptability across mountainous terrain, underwater environments, disaster response, and industrial contexts. Drawing on secondary research, the discussion compares bio-inspired systems, including ALPINE, underwater soft robots, BigDog, SoFi, and HIT_Spibot, with traditional wheeled, legged, and industrial robots. The analysis suggests that bio-inspired movement can improve robotic performance where conventional designs struggle with irregular surfaces, aquatic disturbance, and unpredictable hazards. However, the article also identifies limitations, including technical complexity, energy requirements, material constraints, and the difficulty of translating biological movement directly into engineered systems. It concludes that bio-inspired movement is a significant but not complete route for improving robotic development in unstructured environments.
Does conducting cyberattack simulation exercises enhance institutional preparedness in higher education networks?
Cloud computing, learning management systems, and research data repositories provide higher education institutions with significantly increased attack surfaces, leading to an increase in cyber threats against HEIs. This study aims to examine if cyberattack simulation exercise improves the institution's preparedness in higher education networks. This was done using an empirical research design with primary data gathered from HE institutions that have already run simulations, such as tabletop simulations, cyber range simulations, or incident response exercises. The level of preparedness was assessed on various aspects such as incident detection, response time and coordination, awareness of staff and technical preparedness. The results show that there is a high positive correlation between the conducted simulation exercises and the preparedness indicators. Those institutions that had more frequent and varied simulations had higher mean incident detection scores (4.1 vs. 3.2 on a 5 point scale), a higher mean staff confidence (4.2 vs. 3.0 on a 5 point scale) and faster mean response time readiness (post-simulation mean 4.1 vs. pre-simulation mean 3.2 on a 5 point scale) than those with irregular or no simulation experience. The outcomes suggest that experiential learning in safe-to-fail settings helps to expose hidden weaknesses, define roles in responding and promote intra-departmental collaboration. Yet, constraints and lack of expertise were identified as the challenges affecting the implementation of complex scenario exercises. The study results show that cyberattack simulation exercise is a strategic enabler of institutional cyberresilience rather than just an educational activity. The results offer higher education leaders, cybersecurity professionals, and policymakers actionable insights to bolster their preparedness level in response to the evolving nature of cyber threats.
Does Platelet-Rich Plasma Significantly Improve Postoperative Wound Healing Outcomes in Plastic and Reconstructive Surgery Compared with Standard Care?
The Platelet-Rich Plasma (PRP) is employed extensively in the field of plastic and reconstructive surgeries, because it is believed that the biological capacity of this material provides high concentrations of growth factors which would improve the rate of tissue regeneration. However, it remains debatable whether such capacity could produce any significant effect in comparison with traditional methods of treatment. The main goal of this systematic review will be to assess the effect of platelet-rich plasma on enhancing wound healing results after operations, including healing time, risk of complications, and scar formation, in comparison with the traditional techniques. The synthesis of methods was done based on peer-reviewed meta-analyses, randomized controlled trials, and clinical data published between 2020 and 2025, which were based on such journals as the Journal of Clinical Medicine, Annals of Plastic Surgery, and Acta Biomaterialia. Findings show that PRP has biological plausibility (release of growth factors (PDGF, VEGF, TGF-b)) and demonstrates some patient-reported efficacy. But there is inconsistency in the objective outcome measures. The data of meta-analysis indicate that there is no statistically significant change in the scores of the observer rated Vancouver Scar Scale, at three months. The benefits of healing velocity are marginal (around 2.5 days) and insignificant in mass pooled studies. The cost-effectiveness analysis ranks PRP at some 8635 per quality adjusted life year. In summary, the existing evidence fails to contradict the idea that PRP should be taken as a postoperative procedure that is mandatory in case of the regular plastic and reconstructive surgery. PRP must continue to be a voluntary supplement, which is availed to a select group of patients in a way that has been effectively communicated to them of the peripheral and largely subjective quality of anticipated gains. Before conclusive or definite recommendations can be arrived at, the standardization of the protocols is in dire need.
Does the implementation of machine-learning-based anomaly detection increase the risk of system latency and false-positive trips in automated smart grid controllers compared to traditional regex-based filtering?
This Extended Project Qualification investigates whether machine-learning-based anomaly detection systems introduce greater operational risk in automated smart grid protection controllers, that is, in latency and false-positive circuit-trip rates, than traditional regex-based and signature-based filtering. This project compares three detection methods, including Snort-based rule filtering, an Isolation Forest classifier, and an LSTM Autoencoder using a primary data based on an analysis of the bachirbarika Power System data, a testbed PMU and SCADA dataset consisting of 78,369 rows and 15 different attack scenarios with assistance provided by a review of peer-reviewed literature. Findings indicate that ML-based algorithms identify a significantly higher number of attacks compared to Snort in a setup where the attack is shown as a physical-state anomaly, not as an event on the network layer, but at the tradeoff of introducing a quantifiably higher false positive rate and, in the case of the Isolation Forest, a very great curiousness of inference. LSTM Autoencoder has a more refined portrait with similar accuracy on detection at a lower latency to the baseline of the rule-based. The findings indicate that hybrid-based approach to architecture should be suggested, where rule-based filtering is applied to address the time-sensitive deterministic checks, and the ML models give the context-driven anomaly analysis on both the SCADA and the wide-area layers. All the differences in the latencies were statistically significant, as tested with Mann-Whitney U at p < 0.001.
Limitation Exploration and Optimization Verification of RSA Encryption: using Digital Signature as an example
This dissertation investigates the limitations of RSA encryption and evaluates optimization schemes using digital signatures as the main application example. RSA remains a core asymmetric cryptographic algorithm, but its practical use is constrained by computational cost, key-management complexity, and possible attacks on weak parameter choices. The study combines a literature review of traditional RSA, multi-prime RSA, replaced-modulus RSA, and multi-exponent RSA with a controlled Python experiment. Four algorithms were implemented under the same hardware and software environment, and ten trials were conducted for each algorithm across five stages: key generation, encryption, decryption, signing, and verification. A total of 200 timing data points were analyzed using normalized execution time, acceleration ratios, standard deviation, and box plots. The results show that theoretical optimization does not always translate into practical acceleration. Multi-prime RSA performed worse than the basic algorithm in all stages, with key generation 5.39 times slower and high variability. Replaced-modulus RSA remained close to the baseline while offering potential security advantages, and multi-exponent RSA produced slight improvements in key generation, encryption, and decryption with stable performance. The study concludes that RSA optimization should be evaluated through both security reasoning and empirical performance testing, especially when applied to digital signature systems.
Does Black Tea Residue Mulch Improve Growth in Household Potted Plants? — A study on Epipremnum Hureum, Mentha Haplocalyx, and Allium Fistulosum
This study investigated whether pretreated black tea residue mulch (2.0±0.1 cm) promotes the height and stem diameter of three common household potted plants: Epipremnum aureum (pothos), Mentha haplocalyx (mint), and Allium fistulosum (green onion). A 6-week controlled experiment was conducted with 90 seedlings (30 per species), split into control (no mulch) and treatment (tea residue mulch) groups under consistent environmental conditions (23±2 °C, 50±10% humidity). Weekly measurements of height, stem diameter, soil pH, and moisture were recorded, with two-way ANOVA and curvilinear regression used for statistical analysis. Results confirmed significant growth promotion (p<0.05) from tea residue mulch for all species: mint showed the largest height increase, green onion a late-stage growth amplification, and pothos an early, stable response. Mechanistically, mulch enhanced growth via gradual nutrient release, soil moisture retention, and improved soil microenvironment. This research validates black tea residue as an effective, sustainable organic mulch for home gardening, linking kitchen waste reuse to enhanced potted plant growth, and provides species-specific practical recommendations for gardeners.
Progress and applications of superconducting-nanowire single-photon detectors
Photon detectors connect optical and electrical systems by converting light to electrical signals. Single photon detectors detect single photons which measure ultra-weak light. Traditional semiconductor single-photon detectors suffer from high dark count rates, low infrared detection efficiency, and poor temporal resolution. Superconducting-nanowire single-photon detectors (SNSPDs) overcome these limitations using the hotspot effect. SNSPDs have created a solid foundation for optical technologies and become key in quantum information, LiDARs and Deep-Space Communications. This paper reviews working principles and performance metrics: system detection efficiency, dark count rate, recovery time, and timing jitter. The study summarizes applications in quantum information (quantum key distribution, optical quantum computing), single-photon LiDAR, and deep-space optical communication, replacing most single-photon avalanche diodes (SPADs). Recent experiments show that SNSPD-based systems can achieve meter-scale detection of sea fog, interplanetary laser communication over billions of kilometers, and boson sampling with 20 photons. Ongoing research is expected to improve performance in maximum count rate, system integration, and cryogenic operation, as well as to find applications in emerging fields such as environmental monitoring.
superconducting-nanowire single-photon detectors
Improving Binomial Tree Option Pricing: Estimating Volatility via Linear Regression
The binomial tree option pricing model, proposed by Cox, Ross and Rubinstein, is widely used due to its intuitive discrete-time structure and flexibility in handling various option types. However, the traditional model often assumes constant volatility, which deviates from actual market conditions. This paper proposes an improvement to relax the constant volatility assumption. Specifically, the study first reviews the basic knowledge of the binomial tree model and transforms it into a semi-parametric model. Unlike traditional methods that assume constant volatility, the author employs linear regression to estimate volatility from real option market data. Under the risk-neutral pricing principle, the fitted volatility is embedded into each node of the binomial tree, allowing the model to reflect marketimplied volatility characteristics. Using SSE 50 ETF call option data as the sample, empirical analysis shows that the proposed semi-parametric model significantly reduces pricing errors compared to the traditional binomial tree model. These results indicate that incorporating market implied volatility through information regression enhances the practicality of the binomial tree model. Future research could extend this approach to more complex volatility structures or other types of options and underlying assets.
Wearable perovskite devices in the biomedical field
Perovskites are characterized by excellent optoelectronic properties, low cost and simple manufacturing process, abundant raw materials, and high structure tunability. These are all favorable properties that enable them to be utilized in biomedical applications on a wearable basis. In this paper, the applications of two types of perovskite devices that have a promising prospect in biomedical field: Perovskite Photodetectors (PPDs) and Flexible Perovskite Solar Cells (FPSCs), are analyzed and discussed. In recent times, a number of approaches have been proposed to mitigate the fluctuation of the dark current in PPDs, their poor thermal stability and to enhance the performance of medical devices. The strategies include suppression of dark current by electric field modulation, fabrication of stable black-phase CsPbI3 films at low temperatures and fabrication of ultra-high-sensitivity graphene–perovskite composite fiber detectors. For FPSCs, this paper presents advances on stability enhancement (with dual hole transport layers), efficiency improvement (with liquid crystal elastomer interlayers), and implementation of Perovskite cells as energy source of autonomous sweat sensors under indoor light. These research results show the tremendous application potential of perovskite wearable devices in the biomedical field. Last but not least, this paper addresses future challenges and opportunities in this field, such as stability of long-term operation, scalability of the manufacturing process, and cost control.
Progress in Recycling and Processing of End-of-Life Crystalline Silicon Photovoltaic Modules
The global photovoltaic sector has rapidly developed and enhanced the application of solar energy to its fullest potential. The sector has matured from large scale industrial applications to residential self-generation and self-consumption. Photovoltaic modules are plagued by significant problems associated with their recycling and disposal when they reach the end of their useful lives. This paper identifies the major components of photovoltaic modules and explains the pros and cons of current recycling methods based on their recycling potential and technological challenges. This concern includes the conventional physical delamination, thermal and chemical delamination methods, and other recently proposed separation and recycling methods such as bioleaching, high- voltage pulse and supercritical carbon dioxide foaming methods. Ultimately, the recycling technologies of the future should enable the recovery of the maximum percentage of components and materials to decrease waste and pollution, and at the same time, minimize the energy consumption of the system. Delamination methods should also be environmentally sound, and this should all be systemically integrated to provide a standard for a low carbon for photovoltaic modules in support of the sustainable development of the photovoltaic industry as a whole.
MXene-Based Composites for Electromagnetic Shielding
With the miniaturization and high-frequency development of electronic devices, electromagnetic interference (EMI) has become a critical technical bottleneck and research hotspot. There is an urgent demand for the exploration of lightweight, high-efficiency, and multifunctional shielding materials. Traditional metallic materials suffer from inherent limitations such as high density and poor environmental stability. In this context, developing advanced alternatives with superior shielding effectiveness and broader functional integration has attracted increasing attention. MXene is a novel two-dimensional transition metal carbide/nitride and exhibits great potential in the field of electromagnetic interference shielding because of its excellent electrical conductivity, hydrophilicity, and tunable surface chemistry. This paper reviews the research progress of MXene-based composites. It focuses on performance improvement strategies including composite modification, structural design, and surface modification, as well as their multifunctional applications in flexible electronics, thermal management, environmental adaptability, and integrated energy storage-shielding. Finally, it points out current challenges and future development trends from laboratory research to industrial application.
Environment-Driven Site Selection Model for Mega-Sporting Events
Traditional site selection decision-making for largescale sporting events has long suffered from insufficient attention to environmental sustainability, resulting in serious problems such as high carbon emissions, excessive energy and water consumption, serious waste generation, traffic pollution, and huge ecological pressure. In order to improve the scientificity and green performance of venue selection for mega-sporting events, this study constructs a complete environmental sustainability evaluation system including 6 dimensions and 14 indicators, and formulates the multi-city multi-attribute decision problem as a hybrid-weighted TOPSIS model. The Analytic Hierarchy Process (AHP) is used to measure subjective weights derived from expert experience, and the Entropy Weight Method (EWM) is used to calculate objective weights reflecting real data differences. The two groups of weights are fused under the optimal parameter θ*=0.1378 to obtain stable and reliable comprehensive weights. Then the TOPSIS model is used to rank the environmental sustainability levels of candidate cities. In addition, Monte Carlo simulation and sensitivity analysis are carried out to verify the stability and robustness of the model. The results show that Seattle ranks first with a comprehensive score of 0.822, which can reduce about 30,000 tons of carbon dioxide equivalent emissions in a single event. The correlation coefficient between the model output score and the official NFL rating is as high as 0.973 (p<0.01), demonstrating high accuracy and practical value. This study supplements missing details, optimizes logical consistency, and introduces two innovations: an eventadaptive dynamic weight adjustment mechanism and an environmental risk early warning module. The framework provides a scientific, data-driven, robust, and scalable decision-making tool for green site selection of the Super Bowl, Formula 1, Olympic Games, FIFA World Cup, and other international mega-sporting events.
Dynamic Path Optimization and Personnel Scheduling for Building Emergency Evacuation
Modern buildings feature complex layouts, high occupancy, and mixed functions, making indoor safety inspections critical for emergency evacuation. Traditional inspection strategies rely heavily on experience, often leading to unreasonable paths, missed checks, and delayed evacuation—especially in smoky, low-visibility environments. Existing studies lack an integrated framework that combines standardized sweep protocols, realistic constrained path modeling, and multi-story collaborative scheduling. This paper develops a fullchain emergency evacuation sweep system. For singlefloor scenarios, we model multi-responder cooperative inspection as an Open Multiple Traveling Salesman Problem with Permanent Forbidden Edges and Allowed Revisits (OMTSP-PFE-AR) and solve it using a greedy algorithm. For multi-story composite buildings, a bilevel optimization model is proposed to separate intrafloor scheduling from global personnel configuration. Experiments on a single-story office building and a threestory mixed-use building show that the proposed method achieves full inspection coverage with balanced workloads, shorter sweep times, and lower overall costs. The system adapts to both normal and heavy-smoke conditions, supports professional role matching, and improves the safety and efficiency of real-world building emergency evacuation operations.
Self-Healing Coatings in Extreme Environments
The safety and longevity of materials is put to test in harsh environments. Conventional coating materials cannot fulfill the requirements of long term protection as they cannot repair themselves. Thus, damages that self-heal on coating have become a subject of research. This article systematically reviews the three primary kinds of selfhealing surface namely stimulus-responsive, ceramicbased high-temperature and organic-inorganic composite nature-inspired types of self-healing surface, their healing mechanisms, material innovations, and performance improvements. In particular, the stimulus-reactive types are effective in healing under a mild environment; the ceramic-based high-temperature types can be used in ultrahigh temperature (above 1500°C) with the capability of oxidation; and the nature-inspired composite types can be used in complex multi-field coupling environment. Nevertheless, the discipline continues to encounter such problems as a lack of environmental flexibility and challenges in developing translation. This paper attempts to summarize the developments, evaluate the challenges and present references to how high-performance self-healing coating can be designed and implemented in the future.
Comparison Between Option Pricing model and Discounted Cash Flow model: A study Case of Electronic Arts Company
This paper is a comparative analysis of the Discounted Cash Flow (DCF) model and the binomial option pricing model of Electronic Arts company as a case study. The DCF model will be used to determine the intrinsic value of the firm based on financial and market data obtained through public sources. The value of a call option in the stock of the company within a short period is assessed using the binomial model. These findings indicate that the DCF model gives a firm value of about 36.6 billion USD whereas the binomial model gives an option value of about 14.6 USD. The reason behind this immense difference is the inherent differences in model structure and objectives of valuation. The DCF model is a deterministic and linear model, and the binomial model is a stochastic model with volatility and a convex payoff structure. Further discussion shows that the DCF model is better applied in estimating long-term intrinsic value. The results identify the complementary nature of the two strategies and propose a possibility of having a more in-depth picture of firm value, which might be more relevant in the high uncertainty and innovation industry.
Fault Tree Analysis and Event Tree Analysis: Theory, Comparison and Application in Risk Assessment
Amid the escalating complexity of contemporary industrial and engineering systems, risk evaluation that is both accurate and systematically structured has become increasingly necessary to ensure system safety and to avert major accidents. Fault Tree Analysis (FTA) as well as Event Tree Analysis (ETA) are distinct foundational approaches within probabilistic risk assessment that are taken as the central objects of study. The discussion sets out the theoretical underpinnings, logical analytic procedures, and typical application domains of FTA and ETA; on that basis, a systematic comparison is developed regarding their respective strengths in risk identification, causal analysis, and consequence forecasting. Within this analytical framing, FTA employs deductive reasoning to trace top failure events back to underlying basic causes as well as to the logical combinations of contributing factors that produce those events. ETA proceeds conversely through inductive reasoning: beginning from initiating events, it projects accident evolution pathways and estimates the eventual outcomes associated with those initiating conditions. Widely are these methods deployed across high-risk sectors. In synthesis of these considerations, the paper provides a dependable theoretical foundation for selecting risk assessment models rationally while supporting scientifically grounded decision-making in system safety management together with risk prevention and control.
Benefits Brought by Basic Models of Queuing Theory in Terms of Transportation
This essay is dedicated to emphasizing the importance of basic queuing models, by analyzing with specific examples in the transported situations. The most significant part is improving the intensity of service and reduce the total cost, helping decision makers make the optimism choices both considers the allocation of resources and service methods. This essay focus on two basic queuing models, M/M/1 and M/M/c queuing models. In the specific example of two queuing models, motorway charging is an example. By calculating the probability of vacancy for both ETC and MTC channels, the total cost was estimated. After the calculation for some specific value of the rate that vehicles having ETC, the percentages of ETC channels can be decided correspond to different rate of possessing ETC. In the example of M/M/c queuing model, the parking gate machine is exemplified. The author compares the total cost for two situations with c = 2 and c = 3 . The result of comparison shows the better options. However, those applications are for the preliminary step. The decision makers should improve their own models based on the realistic conditions to be more accurate and closer to the reality, thereby improving the feasibility.
Application of Random Forest Technique in Data Cleaning
Data cleaning is an important part of data preprocessing. Its role is to ensure the accuracy of data analysis and improve model performance. Its main tasks are to identify and resolve various problems in raw data, such as missing values, outliers, and duplicate records, thereby providing strong support for subsequent data mining and modeling. In the era of big data, traditional data cleaning methods suffer from poor adaptability and low accuracy when dealing with high-dimensional, nonlinear, and massive datasets. By contrast, random forest has been widely applied in data cleaning because of its resistance to overfitting, strong ability to handle high-dimensional data, low requirement for complex preprocessing, and relatively strong interpretability. This paper reviews recent domestic and international studies on the principles, methods, and research status of random forest in the three core scenarios of data cleaning. It mainly discusses its advantages over traditional methods, its existing limitations, and future research directions. At the same time, visualized content is incorporated to improve the readability of this review and to provide references for future research and applications.
A Comparative Analysis of Diffusion-Based Models in Text-to-Image Generation
The recent years have seen the rapid development of artificial intelligence image generation methods with the introduction of a wide range of different models to produce images based on a specific text description. The paper explores the design, functionality, and restrictions of four well-known diffusion-based text-to-image generative artificial intelligence systems: Midjourney, DALL-E2, Stable Diffusion, and Imagen. The paper defines the theoretical context of the diffusion probabilistic model and the manner in which it has been applied in the four models of artificial intelligence. This paper uses the comparative and evaluation performance of these models based on the available empirical research studies on the same in relation to image fidelity, prompt adherence, creativity, and bias on various parameters. The results of the analysis show that Stable Diffusion works well in the generation of photorealistic pictures of the human face, Midjourney works well in creativity and visual image in artistic settings, and Imagen works well in complex text description. These models have also exhibited several challenges and limitations. The findings depict the difficulties in the production of precise, just, and imaginative imagery in a variety of situations.
Statistical and Machine Learning Methods for Stock Price Prediction
Financial markets are very dynamic and thus difficult to predict the price of stocks. There are financial theories like the efficient market hypothesis, which indicate that the future prices of stocks are hard to predict. Nonetheless, empirical research also suggests that there can be some patterns and inefficiencies in financial markets. In this paper, the author reviews and analyzes the typical methods that researchers apply to stock price predictions, such as traditional statistical methods, machine learning methods, and deep learning methods. The paper is aimed at comparing the various methods used to analyze financial time series data, including ARIMA and GARCH models, and machine learning algorithms, including the support vector machine, random forest, and neural network. Besides, the latest trends regarding the implementation of deep learning and the utilization of alternative data are explained to give a more comprehensive outlook on the contemporary prediction frameworks. According to the existing literature, machine learning and deep learning models are likely to be more successful in attaining the nonlinear relationship and the temporal dependence in stock prices than traditional statistical models. Nevertheless, their performance is very dependent on the quality of data, selecting features, and the market conditions.
Stock Price Prediction Models: From Multi-Scale Adaptive Networks to Large Language Models
The stock price prediction is an arduous process in the financial time series analysis as it is based on multiscale and non-linear properties of market dynamics. Four recently developed algorithms are discussed in this article: the use of a Multi-Scale Adaptive Decoding Network (MSAD-Net) to identify long term trends and short term volatility, context dependent quantum neural network using quantum batch gradient updating and multi-task learning to predict multi asset distribution returns, classical machine learning models, such as SVMs, CNNs and XGBoost, and a combination between a large language model framework, self-reflection, and reinforcement learning to provide an explanation in a predictive approach. Results of experiments indicate that MSAD-Net can significantly decrease errors in its predictions, the quantum techniques allow efficient parameterized multi-asset correlations, the best overall balance is achieved through XGBoost and the quality of the explanations produced by LLMs is excellent. It follows that hybrid models and additional incorporation of unstructured information are potential areas of further development.
Quantifying Linguistic Divergence: Methodological Evolutions and Case Studies in Lexicostatistics
Lexicostatistics acts as a strong interdisciplinary tool, bringing together the application of statistics and mathematics to language evolution. This paper critically assesses the traditional Swadesh method, highlighting its major steps in the lexicostatistical procedure. It is apparent that, although each step in the traditional method is subject to methodological pitfalls, which greatly impede its accuracy, recent improvements in the method have greatly enhanced its accuracy in glottochronology. From the assessment of the application of lexicostatistics in history, it is apparent that most studies focus on Indo- European languages. This is in line with most linguistic theories, although some aspects in the studies are unique and divergent from conventional theories. This paper, therefore, concludes that lexicostatistics is an emerging field with immense potential for growth, especially through the application of modern technologies like computer intelligence. By extending its scope to other languages, lexicostatistics is likely to give greater and accurate insights into language evolution. This work should be a good guide in the area of Lexicostatistics.
Research on Comparisons with three survival models Comparisons with Decision Tree, Random forests and CNNs
Decision tree, random forests and CNN are three advanced algorithms that can solve classification tasks. This article will compare the performance of the three different algorithms in classification tasks. Here, CNN extracts features through convolution operations to detect objects or recognize images, other two achieve this through building a tree structure. They could help to solve so many problems. Decision tree have been widely applied in fields such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. CNN can be used to detect individual plants or pixels as well as for vegetation classification. Nevertheless, they still encounter challenges in the classification tasks, such as low classification accuracy.
Digital-Intelligent Supply Chain Development Path and Resilience Mechanisms: A Case Study of JD Logistics
Global supply chains are increasingly transitioning from an efficiency-oriented paradigm toward a more resilient and sustainable model [1][2]. Recent disruptions, including geopolitical conflicts, extreme weather events, and trade policy changes, have exposed structural vulnerabilities in traditional supply chain systems, leading to higher operational risks and recovery costs [1][3]. In this context, digital-intelligent technologies—such as big data analytics, artificial intelligence, and digital twins—have become key enablers of supply chain resilience [4][5]. However, many enterprises, particularly small and medium-sized enterprises (SMEs), continue to face challenges such as data silos, fragmented technology adoption, and limited transformation capabilities [3][6]. This study examines JD Logistics as a representative case to explore the development path of digital-intelligent supply chains. The findings indicate that transformation typically follows a phased process, including infrastructure digitization, data integration, and ecosystem collaboration. Moreover, the integration of AI large models, digital twins, and edge computing forms a “prediction–simulation–execution” closed-loop mechanism that enhances adaptability and resilience. Based on these findings, this paper proposes lightweight transformation strategies for SMEs, offering practical implications for supply chain digitalization.
Apple Company Daily Closing Price Prediction Based on Time Series Analysis and Monte Carlo Method
Accurate forecasting of stock prices is critical for quantitative investment and risk management. This paper investigates the methodology and practical application of time series analysis and the Monte Carlo method in predicting AAPL’s daily closing prices. Time series are decomposed into several components so that models are built to capture the patterns of price change and generate forecasts. Empirical results show that the time series model can achieve acceptable prediction accuracy. But the overly smooth curve of price path indicate that the model fails to reflect fluctuations in real market environment. Meanwhile, the Monte Carlo method is used to extract statistical regularities and randomness from price changes. Specifically, it maps sample data to pseudo-random numbers, which are then employed for stochastic price simulation to generate future price paths. The model effectively simulates AAPL’s general trend and realistic fluctuations over the coming trading days, yet its outputs rely on historical data and random sampling and thus should be used with caution. Despite their different mechanisms, both methods yield consistent overall trend results, offering valuable references for trading or investing AAPL stock.
Analysis and Forecast of Leading Sector Rotation in the US Stock Market for 2026
Sector rotation is central to equity allocation because relative industry performance changes with macro conditions, volatility, and investor risk appetite. This study examines the U.S. equity market with monthly data from January 2007 to December 2025 and models the joint dynamics of market variables and five clustered sector groups through a three-state MSIH(3)-VAR(1) framework. The sample combines the S&P 500, VIX, unemployment, credit spreads, an economic growth factor, and industry return clusters constructed from 49 Fama-French industries. Results show that the bull market regime dominates the sample, has the longest average duration, and is associated with lower volatility and stronger technology performance, whereas bear states are shorter and defensive sectors remain relatively resilient. Out-of-sample forecasts for January 2023 to March 2026 achieve high regime consistency but much weaker sector hit rates, implying that macro regimes explain broad market environments better than high-frequency leadership shifts. The findings support regime-switching models as useful tools for strategic allocation, risk monitoring, and structured discussion of sector rotation.
Research on Three-dimensional Reconstruction
Nowadays, with the continuous development of the field of computer vision and computer graphics, 3D reconstruction technology has become an area that has attracted much of people’s attention. 3D reconstruction refers to creating a 3D model of an object in the real world by extracting information from multiple 2D images or sensor data. This technology has important usages in many fields, including virtual reality, augmented reality, cultural heritage preservation, medical image processing, industrial design, etc. This thesis examines the application of 3D reconstruction in various fields, discusses the definition of development history, its wide application in life, and its advantages and disadvantages. This article is divided into four parts and discusses the algorithm research of 3D reconstruction in four fields. Through an in-depth study of 3D reconstruction technology, we can better understand its role in different fields and provide strong support for developing and innovating related applications. As more accurate devices and algorithms continue to advance, 3D reconstruction technology is expected to show its significant potential in more and more fields and create more realistic and useful digital experiences for us.
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