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。

Factors influencing the employed caregiver burden for patients with dementia residing in nursing homes:A cross-sectional study from China

Objective:The institutionalization of care for patients with dementia is becoming a trend.Understanding the burden on employed caregivers and exploring associated factors are of great importance in practice.Therefore,this study aimed to examine the relationship between basic attributes,caring ability,and caregiver burden in employed caregivers practicing in nursing homes.Methods:This cross-sectional study included 541 employed caregivers in 11 four-star nursing homes in Zhejiang Province from April to December 2022.Caregiver burden was assessed using the Zarit Burden Interview(ZBI).Demographic characteristics of participants,characteristics of the older patients with dementia,caring characteristics,training in dementia care,and caring abilities were collected for analysis of influencing factors.A hierarchical multiple regression analysis was conducted to explore the factors influencing the burden on employed caregivers in nursing homes.Results:The ZBI score of employed caregivers in nursing homes was 40.42±10.18,representing a moderate caregiver burden.Factors such as age(U=27.82,P<0.001),residence(U=7.89,P<0.001),educational level(H=55.81,P<0.001),self-care of older patients with dementia(H=85.21,P<0.001),daily care hours(H=73.25,P<0.001),number of older people with dementia cared for(H=14.56,P<0.012)and training in dementia care(U=-9.43,P<0.001)were significantly associated with caregiver burden.Caring ability was negatively associated with caregiver burden(r=-0.22,P<0.01).Furthermore,after controlling for demographic characteristics,the characteristics of older people with dementia,caring characteristics,training in dementia care,and caring ability explained 8.5%,5.8%,and 4.8%of the caregiver burden,respectively.Conclusion:The burden of employed caregivers on patients with dementia in nursing homes can be attributed to various factors.We recommend tailored interventions,such as dementia care training and reviewing the number and duration of hours worked to reduce the burden experienced by caregivers.

Informing policy makers in developing countries:Practices and limitations of geriatric home medication review in Malaysia-A qualitative inquiry

Objective:To explore existing practices and challenges in the delivery of geriatric home medication review(HMR).The study was part of a larger study aimed to offer solution to expand the range of geriatric HMR.Methods:This study employed qualitative exploratory design through semi-structured individual in-depth interviews with the public pharmacists involved in the delivery of geriatric HMR at public hospitals.The purpose of the interviews was to explore challenges faced by them in the delivery of geriatric HMR.Results:Based on the emerging themes from the qualitative data,the study reveals that geriatric HMR in Malaysia is integrated as part of multidisciplinary home care visits,encompassing a diverse patient population with various healthcare needs.However,it faces challenges such as the lack of outcome monitoring,formal training,and workforce constraints.Despite these hurdles,there is a pressing need for the expansion of this service to better serve the community,and collaboration with community pharmacists holds potential to broaden its scope.Ultimately,the findings suggest that pharmacist-led HMR is both warranted and feasible within the Malaysian healthcare context.In order to optimize medicine-use among older people living in the community,approaches for expanding geriatric HMR services in Malaysia must be developed.Conclusions:This study holds profound implications as it attempts to illuminate policy makers in developing countries,enabling them to formulate effective HMR plans.By considering the challenges highlighted within this research,policy makers can design a comprehensive HMR service that caters adeptly to the healthcare needs of the mass population.

Which Neighborhoods Have Easier Access to Online Home Delivery Services?A Spatiotemporal Accessibility Analysis in Nanjing,China

The rise in online home delivery services(OHDS)has had a significant impact on how urban services are supplied and used in recent years.Studies on the spatial accessibility of OHDS are emerging,but few is known about the temporal dimension of OHDS accessibility as well as the geographic and socioeconomic differences in the spatiotemporal accessibility of OHDS.This study measures the spatiotemporal accessibility of four types of OHDS,namely leisure,fresh and convenient,medical,and catering services.The geographic and socioeconomic disparities in the spatiotemporal accessibility of these four types of OHDS are then identified using spatial statistical methods and the Kruskal-Wallis test(K-W test).The case study in Nanjing,China,suggests that:1)spatiotemporal accessibility better reflects the temporal variation of OHDS accessibility and avoids overestimation of OHDS accessibility when only considering its spatial dimension.2)The spatiotemporal accessibility of OHDS varies geographically and socioeconomically.Neighborhoods located in the main city or neighborhoods with higher housing prices,higher population density,and higher point of interest(POI)mix have better OHDS spatiotemporal accessibility.Our study contributes to the understanding of OHDS accessibility from a spatiotemporal perspective,and the empirical insights can assist policymakers in creating intervention plans that take into account variations in OHDS spatiotemporal accessibility.

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.

LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

Determinants of Home Deliveries by Pregnant Mothers in Lumbo Chabbobboma Zone of Gwembe District in Zambia

Background: Home deliveries is still high globally at 42% WHO 2022, due to high home deliveries, maternal death is also high at 43% globally. In sub-Sahara region home deliveries still high. Giving birth at health facilities in most of sub-Saharan African countries Zambia inclusive is still a challenge whereby more than 51% of first-time mothers give birth at home and this gives a risk of high maternal and perinatal deaths. Therefore Reducing number of home deliveries is important to improve maternal and perinatal health issues. In this study, the aim was to investigate the determinants of home deliveries by pregnant mothers in the Luumbo zone of Gwembe district, Zambia. Purpose: Access to skilled care and facilities with capacity to provide emergency and newborn care is critical to reduce maternal death. In Zambia 42% of women still deliveries from home, suggesting a persistent challenge for women to seek, reach, and receive quality maternity care. This study aimed investigate the determinants of home deliveries by pregnant mothers in Luumbo zone of Gwembe district, Zambia. Methods: The study was conducted among postnatal mothers who came for postnatal care at 6 weeks in Luumbo Chabbobboma clinic in Gwembe district southern province of Zambia. This was a descriptive cross-sectional study where a Simple random sampling technique was used to select 105 women of childbearing age who attended postnatal and had a recent delivery. Data were collected using a researcher-administered structured questionnaire to identify determinants of home deliveries in Luumbo Chabbobboma zone. Data analysis was done using SPSS computer software version 27.0. Both descriptive and inferential (chi-square test) analyses were performed and statistical significance was taken at α ≤ 0.05. Results: The results show that 46 (43.8%) respondents were in the age bracket 20 - 29 years. Of the 105 respondents included in the study, 24 (22.9%) of them delivered from home. The results show that high maternal age (p = 0.03), occupation (p = 0.024), distance to the facility (p = 0.014), means of transportation (p = 0.023), multiparity (p = 0.01), timing and number of ANC visits (p Conclusion: From this population. The major reason why women still deliver at home was long distance to the nearest facility. To reduce maternal and perinatal mortality access to health facilities by pregnant women needs to be improved. There should also be active engagement of the traditional and religious institutions in the area.

Neuroarchitecture Analysis in the Mis Abuelitos Nursing Home in Cochachinche, Huanuco, Peru - A Secondary Publication

Neuroarchitecture is a set of characteristics grouped into principles that seek to generate a certain behavior when applied.These principles will vary according to the type of user for whom the design is intended.In this research,these neuroarchitectural principles are identified in the nursing home Mis Abuelitos in Cochachinche,Huánuco.The home was designed and built with notions of nature and the Andean while emphasizing the occupants are the elderly.With this purpose,qualitative research was carried out using two sequential criteria,the first was to identify what these principles are and the second was to recognize the principles within the area.The principles that are assertively used for the elderly occupants were selected.The study observation was performed with observation and photographic sheets and was analyzed with ATLAS.ti processing software.It was obtained that the three neuroarchitectural principles are present in the nursing home which are 67%of the recommended characteristics for the elderly.

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.

Application of horticulture therapy as a health intervention for elderly people in nursing homes:a scoping review

Objective:To review the scope of interventional studies on horticultural therapy(HT)applied to elderly people in nursing institutions to support the efficient implementation of HT among this target group.Methods:In accordance with the scoping review framework proposed by the Joanna Briggs Institute(JBI),the Pub Med,JSTOR,Web of Science,CNKI,and Wanfang databases were searched.Data from the retrieved literature were summarized and analyzed.Results:In all,18 studies were included in this review.The target population groups of interventions included self-supporting elderly people,cognitively impaired elderly people,elderly people with negative emotions,and elderly people with frailty living in nursing institutions.HT interventions,including planting,craft activities,derivative activities,and outdoor viewing activities,are implemented indoors,outdoors,or in mixed settings.The most common duration of the intervention was 8 weeks,the most common frequency was once per week,and the most common session duration was 60 min.Conclusions:The measurements used in HT interventions included assessments of physical,psycho-mental,and social health;quality of life;and activity effects.Future studies should include partially dependent groups and completely dependent groups of elderly individuals,interventions that last at least 6 months,scientifically designed activity intensity and safety-guarantee plans,and outcomeevaluation indicators such as compliance and intervention benefits.

An Integrated Model of Smart Home Applications User Experience Based on Cognitive Dissonance

This study constructs an integrated model of user experience in smart home applications(apps)to deeply explore the impact of cognitive dissonance on users’emotional responses,subsequent behaviors,and experiential outcomes.The research emphasizes the importance of addressing emotional management in the design and development of smart home apps.The findings indicate that emotional response plays a critical mediating role in the user experience of these apps,offering new insights for further optimization.By understanding users’emotional reactions and behavioral patterns under cognitive dissonance,developers can more effectively improve interface design and enhance the overall user experience.

Collision of Two Plates——Market Movement Analysis into Home Made and Imported Cars

China’s car market consists of two plates——domestic made (A) and imported (B) cars. The market has experienced a transition process from the past "B strong v.s. A weak" to today’s "A strong v.s. B weak".

China's Dinosaur Egg Fossil Made its Way Home after Being Smuggled to America for 28 Years

On 28 February 2012, under the close judiciary of and diplomatic cooperation between the United States and China for five years, a fossilized nest containing 22 dinosaur eggs, which was smuggled to America, finally made its way home.

Stress perceptions of nurses serving in home healthcare services during the COVID-19 pandemic:A qualitative research

Objective:To investigate the stress perceptions of nurses serving in home healthcare services during COVID-19.Methods:This study was qualitative research with a phenomenological design.Data were collected and recorded through in-depth interviews with 6 nurses working in MuşState Hospital,Home Healthcare Services Unit using a form consisting of 12 questions on an online platform between May 2021 and July 2021.The audio recordings were transcribed by the researcher and content analysis was performed by creating codes,categories,and themes.Results:The interviews yielded 10 categories and 59 sub-codes.These codes were addressed under the theme of"COVID-19 pandemic".Under this main theme,nurses expressed the problems they experienced in issues such as stress,support mechanisms,and family and social problems during COVID-19.They mentioned that they experienced high stress in this process,as well as social isolation and negative thoughts of society about them and that they could not spare time for themselves and their families.Conclusions:Nurses working in home healthcare services frequently express negativities such as high stress,isolation from society,and increased workload.Therefore,actions should be taken to raise awareness of society on these issues,increase the number of personnel,conduct more research,and share the results with the public.

Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart home

Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,Smart home).To handle these attacks,most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices,especially during Device-to-Device(D2D)interactions.Tapping into the potential of Ethereum blockchain and smart contracts,this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home.The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system.The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system.The simulation was conducted twenty times,each with varying number of devices chosen at random(ranging from 1 to 30).Each requester device sends an arbitrary request with a unique resource requirement at a time.This is done to measure the system's consistency across a variety of device capabilities.The experimental results show that the proposed protocol not only prevents colluding attacks,but also outperforms the benchmark protocols in terms of computational cost,message processing,and response times.

Smart Home IoT Privacy and Security Preservation via Machine Learning Techniques

The development and use of Internet of Things(IoT)devices have grown significantly in recent years.Advanced IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT devices.Corporations have begun to embrace the IoT concept.Identifying true and suitable devices,security faults that might be used for bad reasons,and administration of such devices are only a few of the issues that IoT,a new concept in technological progress,provides.In some ways,IoT device traffic differs from regular device traffic.Devices with particular features can be classified into categories,irrespective of their function or performance.Ever-changing and complex environments,like a smart home,demand this classification scheme.A total of 41 IoT devices were employed in this investigation.To build a multiclass classification model,IoT devices contributed 13 network traffic parameters.To further preprocess the raw data received,preprocessing techniques like Normalization and Dataset Scaling were utilized.Feature engineering techniques can extract features from the text data.A total of 117,423 feature vectors are contained in the dataset after stratification,which are used to further improve the classification model.In this study,a variety of performance indicators were employed to show the performance of the logiboosted algorithms.Logi-XGB scored 80.2%accuracy following application of the logit-boosted algorithms to the dataset for classification,whereas Logi-GBC achieved 77.8%accuracy.Meanwhile,Logi-ABC attained 80.7%accuracy.Logi-CBC,on the other hand,received the highest Accuracy score of 85.6%.The accuracy of Logi-LGBM and Logi-HGBC was the same at 81.37%each.Our suggested Logi-CBC showed the highest accuracy on the dataset when compared to existing Logit-Boosted Algorithms used in earlier studies.

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

Power Scheduling with Max User Comfort in Smart Home:Performance Analysis and Tradeoffs

The smart grid has enabled users to control their home energy more effectively and efficiently.A home energy management system(HEM)is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy.Here,we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm(GWA)and Harmony Search Algorithms(HSA).Moreover,a fusion initiated on HSA and GWA operators is used to optimize energy intake.Furthermore,many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge.Hybridization has proven beneficial in achieving numerous objectives simultaneously,decreasing the peak-to-average ratio and power prices.Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method,Harmony GWA(HGWA).The simulations are for a multifamily housing complex outfitted with various cool gadgets.The simulation results indicate that GWA functions better regarding cost savings than HSA.In reputes of PAR,HSA is significantly more effective than GWA.The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR,as opposed to 503.4 in GWA,398.10 in HSA and 640.3 in HGWA.The suggested approach performed better than HSA and GWA in PAR reduction.For single-family homes in HGWA,GWA and HSA,the reduction in PAR is 45.79%,21.92%and 20.54%,respectively.The hybrid approach,however,performs better than the currently used nature-inspired techniques in terms of Cost and PAR.

Learning-Based Metaheuristic Approach for Home Healthcare Optimization Problem

This research focuses on the home health care optimization problem that involves staff routing and scheduling problems.The considered problem is an extension of multiple travelling salesman problem.It consists of finding the shortest path for a set of caregivers visiting a set of patients at their homes in order to perform various tasks during a given horizon.Thus,a mixed-integer linear programming model is proposed to minimize the overall service time performed by all caregivers while respecting the workload balancing constraint.Nevertheless,when the time horizon become large,practical-sized instances become very difficult to solve in a reasonable computational time.Therefore,a new Learning Genetic Algorithm for mTSP(LGA-mTSP)is proposed to solve the problem.LGA-mTSP is composed of a new genetic algorithm for mTSP,combined with a learning approach,called learning curves.Learning refers to that caregivers’productivity increases as they gain more experience.Learning curves approach is considered as a way to save time and costs.Simulation results show the efficiency of the proposed approach and the impact of learning curve strategy to reduce service times.

Rural implementation of the perioperative surgical home:A case-control study

BACKGROUND Perioperative surgical home(PSH)is a novel patient-centric surgical system developed by American Society of Anesthesiologist to improve outcomes and patient satisfaction.PSH has proven success in large urban health centers by reducing surgery cancellation,operating room time,length of stay(LOS),and readmission rates.Yet,only limited studies have assessed the impact of PSH on surgical outcomes in rural areas.AIM To evaluate the newly implemented PSH system at a community hospital by comparing the surgical outcomes using a longitudinal case-control study.METHODS The research study was conducted at an 83-bed,licensed level-III trauma rural community hospital.A total of 3096 TJR procedures were collected retrospectively between January 2016 and December 2021 and were categorized as PSH and non-PSH cohorts(n=2305).To evaluate the importance of PSH in the rural surgical system,a case-control study was performed to compare TJR surgical outcomes(LOS,discharge disposition,and 90-d readmission)of the PSH cohort against two control cohorts[Control-1 PSH(C1-PSH)(n=1413)and Control-2 PSH(C2-PSH)(n=892)].Statistical tests including Chi-square test or Fischer’s exact test were performed for categorical variables and Mann-Whitney test or Student’s t-test were performed for continuous variables.The general linear models(Poisson regression and binomial logistic regression)were performed to fit adjusted models.RESULTS The LOS was significantly shorter in PSH cohort compared to two control cohorts(median PSH=34 h,C1-PSH=53 h,C2-PSH=35 h)(P value<0.05).Similarly,the PSH cohort had lower percentages of discharges to other facilities(PSH=3.5%,C1-PSH=15.5%,C2-PSH=6.7%)(P value<0.05).There was no statistical difference observed in 90-d readmission between control and PSH cohorts.However,the PSH implementation reduced the 90-d readmission percentage(PSH=4.7%,C1-PSH=6.1%,C2-PSH=3.6%)lower than the national average 30-d readmission percentage which is 5.5%.The PSH system was effectively established at the rural community hospital with the help of team-based coordinated multi-disciplinary clinicians or physician comanagement.The elements of PSH including preoperative assessment,patient education and optimization,and longitudinal digital engagement were vital for improving the TJR surgical outcomes at the community hospital.CONCLUSION Implementation of the PSH system in a rural community hospital reduced LOS,increased directto-home discharge,and reduced 90-d readmission percentages.