基于NodeMCU和Home Assistant的智能家居控制系统

为了提高家居生活的舒适性和便利性,开发了一套智能家居控制系统。该系统采用节点微控制单元(NodeMCU)作为基础硬件,负责收集传感器数据并操控相关执行器件。同时,该系统利用消息队列遥测传输协议(MQTT)和WiˉFi技术构建数据传输链路。此外,该系统利用开源的Home Assistant系统建立了后台服务端,以实现数据的图形化展示。用户可通过安卓移动设备实时监控家居环境状况,并对智能设备实施控制。该系统经过严格测试,表现稳定,显著提高了家居环境的智能化程度。

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

Evaluating Methods of Visual Assistance for Workers to Improve Quality and Usability in Individualized Kitchen Cabinet Assembly

In times of digitalisation, visual assistance systems in assembly are increasingly important. The design of these assembly systems needs to be highly complex to meet the requirements. Due to the increasing number of variants in production processes, as well as shorter innovation and product life cycles, assistance systems should improve quality and reduce complexity of assembly processes. However, many large kitchen manufacturers still assemble kitchen cabinets manually, due to the high variety of components, such as rails and fittings. This paper focuses on the analysis and evaluation of virtual assistance systems to improve quality and usability in individualised kitchen cabinet assembly processes at a large German manufacturer. A solution is identified and detailed.

基于LabVIEW和Vision Assistant的车牌识别系统设计

文章提出了一种基于LabVIEW和Vision Assistant的车牌识别方案。系统使用Vision Assistant的颜色阈值、高级形态学及光学字符识别等函数,实现了对车牌图像的预处理和车牌区域定位以及车牌字符分割、字符训练和识别,使用LabVIEW程序完成车牌的显示。使用数百张车牌图像对该系统进行验证,结果证实该方案是可行的,系统是可靠的,可以快速、准确定位和识别车牌。

KOH-assisted aqueous synthesis of bimetallic metal-organic frameworks and their derived selenide composites for efficient lithium storage

To solve low efficiency,environmental pollution,and toxicity for synthesizing zeolitic imidazolate frameworks(ZIFs)in organic solvents,a KOH-assisted aqueous strategy is proposed to synthesize bimetallic ZIFs polyhedrons,which are used as precursors to prepare bimetallic selenide and N-doped carbon(NC)composites.Among them,Fe–Co–Se/NC retains the three-dimensional(3D)polyhedrons with mesoporous structure,and Fe–Co–Se nanoparticles are uniform in size and evenly distributed.When assessed as anode material for lithium-ion batteries,Fe–Co–Se/NC achieves an excellent initial specific capacity of 1165.9 m Ah·g^(-1)at 1.0 A·g^(-1),and the reversible capacity of Fe–Co–Se/NC anode is 1247.4 m Ah·g^(-1)after 550 cycles.It is attributed to that the uniform composite of bimetallic selenides and N-doped carbon can effectively tune redox active sites,the stable 3D structure of Fe–Co–Se/NCs guarantees the structural stability and wettability of the electrolyte,and the uniform distribution of Fe–Co–S nanoparticles in size esuppresses the volume expansion and accelerates the electrochemical reaction kinetics.

Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

Day by day,biometric-based systems play a vital role in our daily lives.This paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions.This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,etc.The proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and love.The key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and LED.The proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and time.While comparing with existing technologies,the proposed algorithm gave a better error rate and less time.Error rate and time is decreased by 19.79%,5.13 s.for the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT database.The proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.

Grindability Evaluation of Ultrasonic Assisted Grinding of Silicon Nitride Ceramic Using Minimum Quantity Lubrication Based SiO_(2)Nanofluid

Minimum quantity Lubrication(MQL)is a sustainable lubrication system that is famous in many machining systems.It involve the spray of an infinitesimal amount of mist-like lubricants during machining processes.The MQL system is affirmed to exhibit an excellent machining performance,and it is highly economical.The nanofluids are understood to exhibit excellent lubricity and heat evacuation capability,compared to pure oil-based MQL system.Studies have shown that the surface quality and amount of energy expended in the grinding operations can be reduced considerably due to the positive effect of these nanofluids.This work presents an experimental study on the tribological performance of SiO_(2)nanofluid during grinding of Si_(3)N_(4)ceramic.The effect different grinding modes and lubrication systems during the grinding operation was also analyzed.Different concentrations of the SiO_(2)nanofluid was manufactured using canola,corn and sunflower oils.The quantitative evaluation of the grinding process was done based on the amount of grinding forces,specific grinding energy,frictional coefficient,and surface integrity.It was found that the canola oil exhibits optimal lubrication performance compared to corn oil,sunflower oil,and traditional lubrication systems.Additionally,the introduction of ultrasonic vibrations with the SiO_(2)nanofluid in MQL system was found to reduce the specific grinding energy,normal grinding forces,tangential grinding forces,and surface roughness by 65%,57%,65%,and 18%respectively.Finally,regression analysis was used to obtain an optimum parameter combinations.The observations from this work will aid the smooth transition towards ecofriendly and sustainable machining of engineering ceramics.

Managements of Special Health Care Needs Patients from Dental Assistant Point

The main role of this article is to describe dental assistants and the dental department’s role in the dental delivery system for Special health care needs (SCHN) by creating a structure to educate the dental assisting profession and project factors that have a significant impact on the dental assistant. The dental assisting management of SCHN patient’s profession including dental Assistance general responsibilities, regulation credentialing, and education. The SCHN patient’s management creates a challenge in all area of the dental department. Handling patient with a SCHN is underexplored throughout the dental staff especially when there is not enough theoretical foundation, education and training to deal with this category of patients. SCHN patients are defined as individuals who have abnormal behavioral impairments, mental conditions or/and chronic disease or abnormal laboratory results. Thus, the aim of this article is to guide all dental assistance staff in the best dental management choice for SCHN patients. All health care teams are part of patient care in most medical settings. A work environment supportive ensures positive outcomes for patient care. This article was considered as those categories of patients required more care and special way to deal with, moreover language, age, gender and education level of the patient are also considered significant parries. Not Many studies were found to support the care of SCHN patients in dentistry department. Taking experience from other area help to design a system to handle the SCHN even if it requires hiring a Dentist who is specialized in that filed.

Flow characteristics and hot workability of a typical low-alloy high-strength steel during multi-pass deformation

Heavy components of low-alloy high-strength(LAHS) steels are generally formed by multi-pass forging. It is necessary to explore the flow characteristics and hot workability of LAHS steels during the multi-pass forging process, which is beneficial to the formulation of actual processing parameters. In the study, the multi-pass hot compression experiments of a typical LAHS steel are carried out at a wide range of deformation temperatures and strain rates. It is found that the work hardening rate of the experimental material depends on deformation parameters and deformation passes, which is ascribed to the impacts of static and dynamic softening behaviors. A new model is established to describe the flow characteristics at various deformation passes. Compared to the classical Arrhenius model and modified Zerilli and Armstrong model, the newly proposed model shows higher prediction accuracy with a confidence level of 0.98565. Furthermore, the connection between power dissipation efficiency(PDE) and deformation parameters is revealed by analyzing the microstructures. The PDE cannot be utilized to reflect the efficiency of energy dissipation for microstructure evolution during the entire deformation process, but only to assess the efficiency of energy dissipation for microstructure evolution in a specific deformation parameter state.As a result, an integrated processing map is proposed to better study the hot workability of the LAHS steel, which considers the effects of instability factor(IF), PDE, and distribution and size of grains. The optimized processing parameters for the multi-pass deformation process are the deformation parameters of 1223–1318 K and 0.01–0.08 s^(-1). Complete dynamic recrystallization occurs within the optimized processing parameters with an average grain size of 18.36–42.3 μm. This study will guide the optimization of the forging process of heavy components.

Use of cognitive-behavioral career coaching to reduce work anxiety and depression in public employees

BACKGROUND Public employees worldwide are increasingly concerned about work anxiety and depression.Cognitive-behavioral career coaching has emerged as a promising strategy for addressing these mental health disorders,which can negatively impact on a person's overall well-being and performance.AIM To examine whether cognitive-behavioral career coaching reduces work anxiety and depression among Nigerian public employees.METHODS A total of 120 public employees(n=60)suffering from severe anxiety and depression were randomly assigned to the treatment or control groups in this study.Cognitive behavioral coaching was provided twice a week to those in the treatment group,whereas no treatment was given to those in the control group.As part of the study,the Hamilton Anxiety Rating Scales and Beck Depression Inventory were used to collect data.RESULTS Analysis of covariance of the data from participants indicates a significant effect of cognitive-behavioral career coaching on work anxiety and depression.CONCLUSION Insights into the underlying mechanisms by which cognitive behavior career coaching exerts its effects have been gained from this study.Also,the study has gathered valuable data that can inform future practice and guide the development of strategies for supporting mental health at work.

Feasibility Study of Assist-Linked Brief Intervention for Substance Use Disorders in a Rural Community of Plateau State, Nigeria

Objective: We studied the feasibility of using Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) linked with Brief Intervention (ALBI) in treating Substance Use Disorder in Nigeria. There is a clear gap in meeting the needs for treatment and care for people with drug use disorders in Nigeria with many users reporting a self-perceived need for treatment and about 40% of them wanting to receive drug treatment but were unable to access such services. Methods: ASSIST questionnaire was used to gather baseline data, and ALBI along with Motivational Interviewing (MI) was used to intervene for 6 weeks following which ASSIST score was repeated and compared with baseline. Results: Majority of the participants were aged 21 - 30 years, mostly males with (61.3%) lacking steady jobs. Lifetime prevalence of any drug use was (88.2%). Three months prevalence was nicotine (72.0%), alcohol (66.7%), cannabis (47.3%), opioids (11.8%) among others. The ASSIST scores were mostly on the severe ranges. ASSIST result after six weeks showed marked reduction. The Mean Difference (MD) in the ASSIST scores after intervention was significant for tobacco, opioids, alcohol, and cannabis. The Cohen’s D effect size was large for tobacco (1.08), alcohol (0.92) and cannabis (0.73) but low for other substances. Conclusion: ALBI along with MI was helpful in combating the menace of substance use.

Improving fatigue properties of normal direction ultrasonic vibration assisted face grinding Inconel 718 by regulating machined surface integrity

Fatigue properties are crucial for critical aero-engine components in extreme serviceenvironments,which are significantly affected by surface integrity(SI)indexes(especially surface topography,residual stressσ_(res),and microhardness)after machining processes.Normal-direction ultrasonic vibration-assisted face grinding(ND-UVAFG)has advantages in improving the machinability of Inconel 718,but there is a competitive relationship between higher compressiveσ_(res)and higher surface roughness R_(a)in affecting fatigue strength.The lack of a quantitative relationship between multiple SI indexes and fatigue strength makes theindeterminacy of a regulatory strategy for improving fatigue properties.In this work,a model of fatigue strength(σ_f)_(sur)considering multiple SI indexes was developed.Then,high-cycle fatigue tests were carried out on Inconel 718 samples with different SI characteristics,and the influence of ND-UVAFG process parameters on SI was analyzed.Based on SI indexes data,the(σ_f)_(sur)distribution in the grinding surface layer for ND-UVAFG Inconel 718 samples was determined using the developed model,and then the fatigue crack initiation(FCI)sites were furtherpredicted.The predicted FCI sites corresponded well with the experimental results,therebyverifying this model.A strategy for improving the fatigue life was proposed in this work,which was to transfer the fatigue source from the machined surface to the bulk material by controlling the SI indexes.Finally,a critical condition of SI indexes that FCI sites appeared on the surface or in bulk material was given by fitting the predicted results.According to the critical condition,an SI field where FCI sites appeared in the bulk material could be obtained.In this field,thefatigue life of Inconel 718 samples could be improved by approximately 140%.

Advances in magnetic-assisted triboelectric nanogenerators:structures,materials and self-sensing systems

Triboelectric nanogenerators(TENG),renowned for their remarkable capability to harness weak mechanical energy from the environment,have gained considerable attention owing to their cost-effectiveness,high output,and adaptability.This review provides a unique perspective by conducting a comprehensive and in-depth analysis of magnetically assisted TENGs that encompass structures,materials,and self-powered sensing systems.We systematically summarize the diverse functions of the magnetic assistance for TENGs,including system stiffness,components of the hybrid electromagnetic-triboelectric generator,transmission,and interaction forces.In the material domain,we review the incorporation of magnetic nano-composites materials,along with ferrofluid-based TENG and microstructure verification,which have also been summarized based on existing research.Furthermore,we delve into the research progress on physical quantity sensing and human-machine interface in magnetic-assisted TENGs.Our analysis highlights that magnetic assistance extends beyond the repulsive and suction forces under a magnetic field,thereby playing multifaceted roles in improving the output performance and environmental adaptability of the TENGs.Finally,we present the prevailing challenges and offer insights into the future trajectory of the magnetic-assisted TENGs development.

Formulation of Work-Study Combined and Result-Oriented Integrated Curriculum Standards

According to the Annex Technical Regulations for Integrated Curriculum Development(Trial)in Document No.30 of the General Office of the Ministry of Human Resources and Social Security(2012),this paper studies the formulation of the curriculum standards for the integration of Chinese medicinal materials production.We focus on the formulation ideas of the curriculum standards for the integration of Chinese medicinal materials production,the formulation process of the curriculum standards for the integration of Chinese medicinal materials production,including the description of typical work tasks,the determination of curriculum objectives,the analysis of study content,the description of referential study tasks,teaching implementation suggestions,assessment and evaluation suggestions,which can provide a reference for the development and research of other related integrated courses.

Assistive Robotics for Upper Limb Physical Rehabilitation:A Systematic Review and Future Prospects

Physical assistive robotics are oriented to support and improve functional capacities of people.In physical rehabilitation,robots are indeed useful for functional recovery of affected limb.However,there are still open questions related to technological aspects.This work presents a systematic review of upper limb rehabilitation robotics in order to analyze and establish technological challenges and future directions in this area.A bibliometric analysis was performed for the systematic literature review.Literature from the last six years,conducted between August 2020 and May 2021,was reviewed.The methodology for the literature search and a bibliometric analysis of the metadata are presented.After a preliminary search resulted in 820 articles,a total of 66 articles were included.A concurrency network and bibliographic analysis were provided.And an analysis of occurrences,taxonomy,and rehabilitation robotics reported in the literature is presented.This review aims to provide to the scientific community an overview of the state of the art in assistive robotics for upper limb physical rehabilitation.The literature analysis allows access to a gap of unexplored options to define the technological prospects applied to upper limb physical rehabilitation robotics.

Inquiring Natural Language Processing Capabilities on Robotic Systems through Virtual Assistants:A Systemic Approach

This paper attempts to approach the interface of a robot from the perspective of virtual assistants.Virtual assistants can also be characterized as the mind of a robot,since they manage communication and action with the rest of the world they exist in.Therefore,virtual assistants can also be described as the brain of a robot and they include a Natural Language Processing(NLP)module for conducting communication in their human-robot interface.This work is focused on inquiring and enhancing the capabilities of this module.The problem is that nothing much is revealed about the nature of the human-robot interface of commercial virtual assistants.Therefore,any new attempt of developing such a capability has to start from scratch.Accordingly,to include corresponding capabilities to a developing NLP system of a virtual assistant,a method of systemic semantic modelling is proposed and applied.For this purpose,the paper briefly reviews the evolution of virtual assistants from the first assistant,in the form of a game,to the latest assistant that has significantly elevated their standards.Then there is a reference to the evolution of their services and their continued offerings,as well as future expectations.The paper presents their structure and the technologies used,according to the data provided by the development companies to the public,while an attempt is made to classify virtual assistants,based on their characteristics and capabilities.Consequently,a robotic NLP interface is being developed,based on the communicative power of a proposed systemic conceptual model that may enhance the NLP capabilities of virtual assistants,being tested through a small natural language dictionary in Greek.

Resilience-Oriented Approach to the Control of Ventricular Assist Devices

Context: Advanced heart failure (AHF) poses a global challenge, where heart transplantation is a treatment option but limited by donor scarcity. Proposal: This study aims to enhance the performance of ventricular assist devices (VADs) in the face of adverse events (AEs) using a resilience-based approach. The objective is to develop a method for integrating resilience attributes into VAD control systems, employing dynamic risk analysis and control strategies. Results: The outcomes include a resilient control architecture enabling anticipatory, regenerative, and degenerative actions in response to AEs. A method of applied resilience (MAR) based on dynamic risk management and resilience attribute analysis was proposed. Conclusion: Dynamic integration between medical and technical teams allows continuous adaptation of control systems to meet patient needs over time, improving reliability, safety, and effectiveness of VADs, with potential positive impact on the health of heart failure patients.

Hierarchical Privacy Protection Model in Advanced Metering Infrastructure Based on Cloud and Fog Assistance

The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers.However,with the advancement of information and communication technology,new security and privacy challenges have emerged for AMI.To address these challenges and enhance the security and privacy of user data in the smart grid,a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance(HPPM-AMICFA)is proposed in this paper.The proposed model integrates cloud and fog computing with hierarchical threshold encryption,offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid.The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance,thereby assigning appropriate protection levels.Furthermore,a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs,ensuring secure aggregation and encryption of user data.Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs,thereby safeguarding user data in the smart grid.

Clinical Study of Tianji Robotic-Assisted Surgery for Upper Cervical Spine Fractures

Object: To compare the safety, clinical efficacy, and complication rate of “Tianji” robot-assisted surgery with traditional open surgery in the treatment of cervical vertebrae fracture. Methods: 60 patients with upper cervical vertebrae fracture admitted to Baise People’s Hospital between November 2018 and April 2024 were retrospectively analyzed. Among these patients, 29 underwent “Tianji” robot-assisted surgery (Robot group), and 31 underwent traditional C-arm fluoroscopy-assisted open surgery (Open group). Statistical analysis of the data was performed using SPSS 27.0 software to compare general data (gender, age, BMI), preoperative and postoperative visual analogue scale (VAS) scores, neck disability index (NDI), intraoperative blood loss, accuracy of screw placement on imaging, and the number of complications in both groups for comprehensive evaluation. A P value < 0.05 was deemed to have achieved statistical significance. Results: There was no significant difference in preoperative VAS scores between the two groups (Robot group: 8.34 ± 0.61;Open group: 8.26 ± 0.68, P = 0.317). There was also no significant difference in VAS scores at 1 week postoperatively (Robot group: 6.90 ± 0.31;Open group: 6.94 ± 0.36, P = 0.3237). Preoperative NDI scores showed no significant difference between the two groups (Robot group: 43.31 ± 2.67;Open group: 43.84 ± 2.67, P = 0.2227), and the difference in NDI scores at 1 week postoperatively was also not significant (Robot group: 35.69 ± 4.24;Open group: 37.35 ± 3.48, P = 0.0509). Intraoperative blood loss in the Robot group was significantly lower than in the Open group (246.21 ± 209 ml vs 380.65 ± 328.04 ml, P = 0.0308), with a statistically significant difference. The operation time was longer in the Robot group (3.75 ± 0.74 h) compared to the Open group (2.74 ± 0.86 h). In terms of screw placement accuracy, the Robot group had a higher accuracy rate for Class A screws compared to the Open group (102 screws vs 94 screws, P = 0.0487), and the accuracy rate for Class B screws was also higher in the Robot group (13 screws vs 29 screws, P = 0.0333), with both differences being statistically significant. There was no significant difference in the number of complications between the two groups (Robot group: 8 cases;Open group: 10 cases, P = 0.6931). Conclusion: Patients treated with “Tianji” robot-assisted surgery for upper cervical vertebrae fracture had lower intraoperative blood loss and higher screw placement accuracy compared to those undergoing traditional C-arm fluoroscopy-assisted open surgery, indicating that this robot-assisted surgery can effectively reduce intraoperative blood loss and improve screw placement accuracy.