Environmental impact improvements due to introducing automation into underground copper mines

A life-cycle assessment(LCA) model was developed to comparatively analyze the use of manual and automated mining equipment in underground copper mine sites.Processes and key variables that were determined to contribute to the environmental impact of operations were identified for six mine sites in a range of geographical locations around the world.Our model successfully calculated carbon dioxide(CO_(2) eq.) emissions to within 4.9% of the reported annual emissions from the site's respective companies.The implementation of automation was found to decrease global warming potential by a range of 11.4%-18.0% or 3.9-17.9 kg CO_(2) eq./t ore.The model was also used to estimate the average reductions across several impact potentials including,acidification(11.9%-17.8%),eutrophication(7.6%-13.7%),and human toxicity(16.0%-20.0%).World-wide the mining industry is moving toward introducing significantly more automation to enhance productivity and safety.This novel work demonstrates an important third dimension that can support this move,reduced environmental impact.

Automation 5.0: The Key to Systems Intelligence and Industry 5.0

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.

基于Automation Studio仿真软件的液压与气动技术人才培养实践

通过对《液压与气动技术》课程在教学过程中的问题盘点,总结出教学中存在的实验资源少、设备陈旧、成本高、利用率低等痛点,Automation Studio仿真软件具有强大的系统仿真与动画设计功能,有针对液压与气动及电气系统的功能模块,将Automation Studio仿真软件引入课堂教学可以很好的弥补上述问题,利用液压气动系统与电气系统的仿真功能,设计相应的实践教学课程内容,分别对课前、课中、课后三个环节的具体实施过程及要求进行详细阐述,通过对软件的教、学、用达到提升课程教学效果的目的。

Anti-aging performance improvement and enhanced combustion efficiency of boron via the coating of PDA

Boron is an ambitious fuel in energetic materials since its high heat release values,but its application is prohibited by low combustion efficiency and oxidization during storage.The polydopamine(PDA)was introduced into boron particles,investigating the impact of PDA content on the energetic behavior of boron.The results indicated that the PDA coating formed a fishing net structure on the surface of boron particles.The heat release results showed that the combustion calorific value of B@PDA was higher than that of the raw boron.Specifically,the actual combustion heat of boron powder in B@10%PDA increased by 38.08%.Meanwhile,the DSC peak temperature decreased by 100.65℃under similar oxidation rate compared to raw boron.Simultaneously,the B@PDA@AP and B@AP composites were prepared,and their combustion properties were evaluated.It was demonstrated that B@10%PDA@AP exhibited superior performance in terms of peak pressure and burning time,respectively.The peak pressure is 12.43 kPa more than B@AP and burning time is 2.22 times higher than B@AP.Therefore,the coating of PDA effectively inhibits the oxidization of boron during storage and enhances the energetic behavior of boron and corresponding composites.

Enhancing Private Healthcare Effectiveness in Lagos State, Nigeria: An Overview of the Effect of Quality Improvement Initiatives and Implications for Sustainable Healthcare Delivery

Background: Nigeria, a nation grappling with rapid population growth, economic intricacies, and complex healthcare challenges, particularly in Lagos State, the economic hub and most populous state, faces the challenge of ensuring quality healthcare access. The overview of the effect of quality improvement initiatives in this paper focuses on private healthcare providers in Lagos State, Nigeria. The study assesses the impact of donor-funded quality improvement projects on these private healthcare facilities. It explores the level of participation, perceived support, and tangible effects of the initiatives on healthcare delivery within private healthcare facilities. It also examines how these initiatives influence patient inflow and facility ratings, and bring about additional benefits and improvements, provides insights into the challenges faced by private healthcare providers in implementing quality improvement projects and elicits recommendations for improving the effectiveness of such initiatives. Methods: Qualitative research design was employed for in-depth exploration, utilizing semi-structured interviews. Private healthcare providers in Lagos involved in the SP4FP Quality Improvement Project were purposively sampled for diversity. Face-to-face interviews elicited insights into participation, perceived support, and project effects. Questions covered participation levels, support perception, changes observed, challenges faced, and recommendations. Thematic analysis identified recurring themes from interview transcripts. Adherence to ethical guidelines ensured participant confidentiality and informed consent. Results: Respondents affirmed active involvement in the SP4FP Quality Improvement Project, echoing literature emphasizing private-sector collaboration with the public sector. While acknowledging positive influences on facility ratings, respondents highlighted challenges within the broader Nigerian healthcare landscape affecting patient numbers. Respondents cited tangible improvements, particularly in staff management and patient care processes, validating the positive influence of quality improvement projects. Financial constraints emerged as a significant challenge, aligning with existing literature emphasizing the pragmatic difficulties faced by private healthcare providers. Conclusions: This study illuminates the complex landscape of private healthcare provision in Lagos State, emphasizing the positive impact of donor-funded quality improvement projects. The findings provide nuanced insights, guiding policymakers, healthcare managers, and practitioners toward collaborative, sustainable improvements. As Nigeria progresses, these lessons will be crucial in shaping healthcare policies prioritizing population well-being.

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.

Selection criteria of MPOB-Angola germplasm collection for yield improvement of the oil palm

Oil palm germplasm collected from Angola,Africa in 1991 were subjected to genetic variability potential studies.The collection was planted in the form of open-pollinated families as trials at the Malaysian Palm Oil Board(MPOB)Kluang Research Station,Johor,Malaysia,in 1994.Dura palms from 52 families and tenera palms from 44 families of MPOB-Angola were evaluated for their bunch yield and bunch quality components.The objectives of this study were to determine the genetic variability among the families and performance of MPOB-Angola germplasm for yield improvement.The analysis of variance(ANOVA)revealed highly significant differences between the dura and tenera families for most of the traits,suggesting the presence of high genetic variability,which is essential for breeding programmes.Among the duras,family AGO 02.02 displayed the best yield performance,with a high fresh fruit bunch,oil yield and total economic product at 240.40,29.46 and 37.93 kg palm^(-1)year^(-1),respectively.As for the teneras,family AGO 03.04 recorded the highest FFB yield and oil yield at 249.25 and 45.22 kg palm^(-1)year^(-1),respectively.Besides that,several families with big fruit sizes or producing a mean fruit weight of 14-17 g were also identified.Both dura and tenera from AGO 01.01 recorded the highest oil to bunch(O/B)of 17.76%and 28.65%,respectively.These findings will facilitate the selection of palms from the MPOB-Angola germplasm for future breeding programmes.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

Effects of Microbial Fertilizers on Soil Improvement and Wheat Growth Characteristics in Saline-alkali Land

[Objectives]This study was conducted to further enrich the research on saline-alkali land improvement,and explore the effects of biological bacterial fertilizers containing Bacillus subtilis and Bacillus velezensis HM-3 in saline-alkali land improvement and crop growth promotion.[Methods]Wheat was planted in saline-alkali land in Huanghua City,Hebei Province,and a mixed application experiment was carried out using biological agents from Hemiao Biotechnology Co.,Ltd.[Results]Compared with the field of control check(CK),water-soluble salts and pH value in the experimental fields decreased,and living bacteria count in the soil increased.Meanwhile,the economic characters of wheat in the experimental fields showed excellent performance,with yields increasing by 39.09%and 27.49%compared with the CK.It could be seen that the application of biological bacterial fertilizers achieved obvious effects of improving saline-alkali soil and increasing wheat yield.[Conclusions]In this study,the effects of biological bacterial fertilizers on saline-alkali land and wheat growth characters were clarified,providing some technical support and theoretical guidance for wheat planting in Huanghua saline-alkali land.

In situ digital testing method for quality assessment of soft soil improvement with polyurethane

This study purposes an in situ testing method on quality assessment of soil improvement.Factual drilling data includes the spatial distribution and in situ strength of untreated and treated soil along three different drillholes measured by on-site drilling monitoring method.These factual drilling data can characterize the degree of soil improvement by penetration injection with permeable polyurethane.Result from on-site drilling monitoring shows that the linear zones represent constant drilling speeds shown in the plot of drill bit advancement vs.net drilling time,which indicates the spatial distributions of soil profile.The soil profile at the study site is composed of four layers,which includes fill,untreated silty clay,treated silty clay,and mucky soil.The results of soil profile are verified by the parallel site loggings.The constant drilling speeds profile the coring-resistant strength of drilled soils.By comparing with the untreated silty clay,the constant drilling speeds of the treated silty clay have been decreased by 13.0-62.8%.Two drilling-speed-based indices of 61.2%and 65.6%are proposed to assess the decreased average drilling speed and the increased in situ strength of treated silty clay.Laboratory tests,i.e.uniaxial compressive strength(UCS)test,have been performed with core sample to investigate and characterize in situ strength by comparing that with drilling speeds.Results show that the average predicted strengths of treated silty clay are 2.4-6.9 times higher than the average measured strength of untreated silty clay.The UCS-based indices of 374.5%and 344.2%verified the quality assessment(QA)results by this new in situ method.This method provides a cost-effective tool for quality assessment of soil improvement by utilizing the digital drilling data.

基于Automation Studio的液压动力滑台控制系统仿真设计

动力滑台是组合机床实现进给运动的通用部件,其控制系统是实现组合机床自动化或半自动化的关键环节。在分析动力滑台工作过程及液压控制回路的基础上,基于Automation Studio进行了控制系统仿真设计,主要包括液压系统建模、PLC控制系统建模及软件设计,仿真结果满足各种控制要求。

Post-operative morbidity after neoadjuvant chemotherapy and resection for gallbladder cancer: A national surgical quality improvement program analysis

BACKGROUND Gallbladder cancer is the most common malignancy of the biliary tract.Neo-adjuvant chemotherapy(NACT)has improved overall survival by enabling R0 resection.Currently,there is no consensus of guidelines for neoadjuvant therapy in gallbladder cancer.As investigations continue to analyze the regimen and benefit of NACT for ongoing care of gallbladder cancer patients,we examined American College of Surgeons National Surgical Quality Improvement Program(NSQIP)database to determine if there was higher morbidity among the neo-adjuvant group within the 30-day post-operative period.We hypothesized patients who underwent NACT were more likely to have higher post-operative morbidity.AIM To investigate the 30-day post-operative morbidity outcomes between patients who received NACT and underwent surgery and patients who only had surgery.METHODS A retrospective analysis of the targeted hepatectomy NSQIP data between 2015 and 2019 was performed to determine if NACT in gallbladder cancer increased the risk for post-operative morbidity(bile leak,infection rate,rate of converting to open surgery,etc.)compared to the group who only had surgery.To calculate the odds ratio for the primary and secondary outcomes,a crude logistic regression was performed.RESULTS Of the 452 patients,52 patients received NACT prior to surgery.There were no statistically significant differences in the odds of morbidity between the two groups,including bile leak[odds ratio(OR),0.69;95%confidence interval(95%CI):0.16-2.10;P=0.55],superficial wound infection(OR,0.58;95%CI:0.03-3.02;P=0.61),and organ space wound infection(OR,0.63;95%CI:0.18-1.63;P=0.61).CONCLUSION There was no significant difference in the risk of 30-day post-operative morbidity between the NACT and surgery group and the surgery only group.

Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

A Home Automation Control System Based on LabVIEW

The objective of this project is to explore the possibility of using X-10 and LabVIEW to control the device in the house. Based on the serial port communication of LabVIEW, the X-10 module can be programmed by using the X-10 commands. Through the power line, all the devices connected to the socket will be controlled. Without replacing the existing wire, it must be an easy control system for the user who has no experience in electronics or communication engineering. Actually, this is a quite practical X-10 home automation system.

Quieting the neonatal intensive care unit:A quality improvement initiative

BACKGROUND The neonatal intensive care unit(NICU)is vital for preterm infants but is often plagued by harmful noise levels.Excessive noise,ranging from medical equipment to conversations,poses significant health risks,including hearing impairment and neurodevelopmental issues.The American Academy of Pediatrics recommends strict sound limits to safeguard neonatal well-being.Strategies such as education,environmental modifications,and quiet hours have shown to reduce noise levels.However,up to 60%of the noises remain avoidable.High noise exposure exacerbates physiological disturbances,impacting vital functions and long-term neurological outcomes.Effective noise reduction in the NICU is crucial for promoting optimal neonatal development.AIM To measure the sound levels in a NICU and reduce ambient sound levels by at least 10%from baseline.METHODS A quasi-experimental quality improvement project was conducted over 4 mo in a 20-bed level 3 NICU in a tertiary care medical college.Baseline noise levels were recorded continuously using a sound level meter.The interventions included targeted education,environmental modifications,and organizational changes,and were implemented through three rapid Plan-Do-Study-Act(PDSA)cycles.Weekly feedback and monitoring were conducted,and statistical process control charts were used for analysis.The mean noise values were compared using the paired t-test.RESULTS The baseline mean ambient noise level in the NICU was 67.8 dB,which decreased to 50.5 dB after the first cycle,and further decreased to 47.4 dB and 51.2 dB after subsequent cycles.The reduction in noise levels was 21%during the day and 28%PDSA cycle(mean difference of−17.3 dB,P<0.01).Peak noise levels decreased from 110 dB to 88.24 dB after the intervention.CONCLUSION A multifaceted intervention strategy reduced noise in the NICU by 25%over 4 months.The success of this initiative emphasizes the significance of comprehensive interventions for noise reduction.

Robotic Process Automation with New Future Trends

The invention concept of Robotic Process Automation (RPA) has emerged as a transformative technology that has revolved the local business processes by programming repetitive task and efficiency adjusting the operations. This research had focused on developing the RPA environment and its future features in order to elaborate on the projected policies based on its comprehensive experiences. The current and previous situations of industry are looking for IT solutions to fully scale their company Improve business flexibility, improve customer satisfaction, improve productivity, accuracy and reduce costs, quick scalability in RPA has currently appeared as an advance technology with exceptional performance. It emphasizes future trends and foresees the evolution of RPA by integrating artificial intelligence, learning of machine and cognitive automation into RPA frameworks. Moreover, it has analyzed the technical constraints, including the scalability, security issues and interoperability, while investigating regulatory and ethical considerations that are so important to the ethical utilization of RPA. By providing a comprehensive analysis of RPA with new future trends in this study, researcher’s ambitions to provide valuable insights the benefits of it on industrial performances from the gap observed so as to guide the strategic decision and future implementation of the RPA.

High rate of clinically relevant improvement following anatomical total shoulder arthroplasty for glenohumeral osteoarthritis

BACKGROUND The minimal clinically important difference(MCID)is defined as the smallest meaningful change in a health domain that a patient would identify as important.Thus,an improvement that exceeds the MCID can be used to define a successful treatment for the individual patient.AIM To quantify the rate of clinical improvement following anatomical total shoulder arthroplasty for glenohumeral osteoarthritis.METHODS Patients were treated with the Global Unite total shoulder platform arthroplasty between March 2017 and February 2019 at Herlev and Gentofte Hospital,Denmark.The patients were evaluated preoperatively and 3 months,6 months,12 months,and 24 months postoperatively using the Western Ontario Osteoarthritis of the Shoulder index(WOOS),Oxford Shoulder Score(OSS)and Constant-Murley Score(CMS).The rate of clinically relevant improvement was defined as the proportion of patients who had an improvement 24 months postoperatively that exceeded the MCID.Based on previous literature,MCID for WOOS,OSS,and CMS were defined as 12.3,4.3,and 12.8 respectively.RESULTS Forty-nine patients with a Global Unite total shoulder platform arthroplasty were included for the final analysis.Mean age at the time of surgery was 66 years(range 49.0-79.0,SD:8.3)and 65%were women.One patient was revised within the two years follow-up.The mean improvement from the preoperative assessment to the two-year follow-up was 46.1 points[95%confidence interval(95%CI):39.7-53.3,P<0.005]for WOOS,18.2 points(95%CI:15.5-21.0,P<0.005)for OSS and 37.8 points(95%CI:31.5-44.0,P<0.005)for CMS.Two years postoperatively,41 patients(87%)had an improvement in WOOS that exceeded the MCID,45 patients(94%)had an improvement in OSS that exceeded the MCID,and 42 patients(88%)had an improvement in CMS that exceeded the MCID.CONCLUSION Based on three shoulder-specific outcome measures we find that approximately 90%of patients has a clinically relevant improvement.This is a clear message when informing patients about their prognosis.

An Overview of Soil Improvement through Ground Grouting

Soil is an essential component of what surrounds us in nature, providing as the basis for our infrastructure and construction. However, soil is not always suitable for construction due to a variety of geotechnical issues such as inadequate bearing capacity, excessive settlement, and liquefaction susceptibility. Through improving the engineering qualities of soil, such as strength, permeability, and stability, ground grouting is a specific geotechnical method used. Using a fluid grout mixture injected into the subsurface, holes are filled and weak or loose strata are solidified as the material seeps into the soil matrix. The approach’s adaptability in addressing soil-related issues has made it more well-known in the fields of civil engineering and construction. In the end, this has improved groundwater management, foundation support, and overall geotechnical performance.

Instrument Automation Control System Faults and Maintenance

This article explores the topic of fault diagnosis and maintenance strategies for instrument automation control systems,analyzing them through specific cases.The aim of this research is to improve the stability and reliability of the system by conducting a thorough investigation of faults and maintenance in instrument automation control systems.By doing so,this research hopes to provide a strong guarantee for the smooth progress of industrial production.