Whole-process case management effects on mental state and selfcare ability in patients with liver cancer

BACKGROUND Regarding the incidence of malignant tumors in China,the incidence of liver cancer ranks fourth,second only to lung,gastric,and esophageal cancers.The case fatality rate ranks third after lung and cervical cancer.In a previous study,the whole-process management model was applied to patients with breast cancer,which effectively reduced their negative emotions and improved treatment adherence and nursing satisfaction.METHODS In this single-center,randomized,controlled study,60 randomly selected patients with liver cancer who had been admitted to our hospital from January 2021 to January 2022 were randomly divided into an observation group(n=30),who received whole-process case management on the basis of routine nursing mea-sures,and a control group(n=30),who were given routine nursing measures.We compared differences between the two groups in terms of anxiety,depression,the level of hope,self-care ability,symptom distress,sleep quality,and quality of life.RESULTS Post-intervention,Hamilton anxiety scale,Hamilton depression scale,memory symptom assessment scale,and Pittsburgh sleep quality index scores in both groups were lower than those pre-intervention,and the observation group had lower scores than the control group(P<0.05).Herth hope index,self-care ability assessment scale-revision in Chinese,and quality of life measurement scale for patients with liver cancer scores in both groups were higher than those pre-intervention,with higher scores in the observation group compared with the control group(P<0.05).CONCLUSION Whole-process case management can effectively reduce anxiety and depression in patients with liver cancer,alleviate symptoms and problems,and improve the level of hope,self-care ability,sleep quality,and quality of life,as well as provide feasible nursing alternatives for patients with liver cancer.

Lagos State Hospital Waste Management Practices: A Descriptive Overview with Stakeholders’ Role and Key Recommendations

Background: Waste generation and its disposal is an essential issue in the sustainability of the environment and the planet’s future. Waste management is essential across sectors, likewise the health sector. Therefore, there is a need to employ extra care and attention to handling waste generated from healthcare facilities to avoid the dangers of poor biomedical waste management. We carried out this study to examine the waste management practice in healthcare facilities in Lagos State. Methods: The study was a descriptive survey carried out in one-thousand two hundred and fifty-six (1256) healthcare facilities in Lagos State. Nine hundred sixty-nine (969) of these facilities are located in urban areas, while two hundred and eighty-seven (287) are rural. The facilities studied are government/public health facilities (15.45%), private-for-profit facilities (82.88%), NGOs, Mission/Faith-Based medical facilities (1.67%). The data collected were analyzed using descriptive statistics. Specifically, we utilized bar charts, frequency, and percentage. Result: The result shows that 98.4% (1236) of the studied facilities are registered with the Lagos State Waste Management Authority (LAWMA), while 1.6% (20) are not registered. 98.5% (191) of the 194 government-owned facilities, 98.5% (1025) of the 1041 private-for-profit facilities, and 98.2% (20) of the 21 NGOs/faith-based health facilities are registered with Lagos State Waste Management Authority. The result also shows that 94% of the healthcare facilities studied in Lagos State use color-coded waste bags to segregate waste at the point of origin. 58.7% of the facilities use red-colored bags, 33.3% use yellow-colored bags, 10.7% use black-colored bags, and 1.3% use brown biohazard bags for segregating Infectious waste. Also, 34.2% of the health facilities in Lagos state use red-colored bags, 36.9% use yellow-colored bags, 11% use black-colored bags, and 4.1% use brown-colored bags to segregate their hazardous waste. Conclusion: Some healthcare facilities in Lagos State do not follow the recommended guidelines for medical waste segregation. Waste generated is not appropriately segregated at the point of origin into the recommended colored bags/bins in some facilities. Thus, a policy and procedure regulating healthcare waste are mandatory. It is important to regularly train healthcare workers on proper waste management practices and encourage staff to read and apply WHO rules in managing healthcare waste. Healthcare personnel should realize that hazardous material is a potential cause of a public disaster.

Comparison of water resources management between China and the United States

As the world’s top two economies,the United States(U.S.)and China face a number of similar water resources problems.Yet,few studies have been done to systematically compare policies and approaches on water resources management between China and the U.S.This study compares water resources policies of China and the U.S.in the areas of national authority,water supply,water quality,and ecosystem use of the water to draw lessons learned and shed light on water resources management in China,the U.S.,and the rest of the world.The lessons learned from the comparison include six aspects.1)New paradigms of people-water harmony and a water-saving society are urgently needed to address the pressing water crisis and achieve the United Nations Sustainable Development Goals(UN SDGs).2)A comprehensive,consistent,forward-looking national policy is necessary to achieve sustainable use of water resources.3)Empowerment of river basin commissions with comprehensive authority over the integrative management of air,land,water,and biological resources in the river basin could significantly enhance the benefits and effectiveness of economic development and environmental protection.4)Expansion of water exchange through market mechanisms among water users promotes efficient and beneficial water uses.5)Use of water for ecosystem services should be an integral part of water resources management.China has set up a national blueprint for achieving ecological civilization;maintaining appropriate amounts of flow in rivers and lakes for maintenance of wildlife and fisheries and ecosystems should be institutionalized as part of this national strategy as well.6)By sharing their rich experiences and lessons in water resources management,economic development,and ecological protection with other countries,China and the U.S.can help the world to achieve global human-water harmony and the UN SDGs.

A new state of charge determination method for battery management system

State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.

Changing Vegetation Patterns in Yobe State Nigeria: An Analysis of the Rates of Change, Potential Causes and the Implications for Sustainable Resource Management

The exploitation of natural resources for timber production, fuelwood use and conversion to agricultural land is increasing to such an extent that the sustainable use of many areas of the world is in doubt. This paper examines three decades of freely available Landsat satellite images of the northeastern part of Nigeria using a supervised classification based technique to create maps of vegetation change in Yobe State. The maps are then used to examine the temporal and spatial aspects of changes which have occurred in the context of previous evidence and literature. The results indicate that the vegetation of the area has drastically reduced since the 1970’s. However, as this study shows, the pattern of these changes is complicated and cannot be explained by any single physical or anthropogenic causal factor. Similarly, evidence from ground truthing investigation indicates the importance of fuelwood collection to the deforestation process within the region. This article shows the value of an existing remote sensing and image processing methodology for the assessment of vegetation change in developing countries in relation to the sustainable management of natural resources. The study also discusses the overall change within the study area and discusses several potential causative factors of the observed patterns of change.

A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.

Study on the Management of Chronic Diseases in American and British Community Pharmacy

Objective To provide a reference for promoting the construction of chronic disease management in community pharmacies in China.Methods Literature research and comparative research methods were used to analyze the management of chronic disease carried out by community pharmacies in the United States and the United Kingdom.Results and Conclusion The management of chronic diseases in American and British community pharmacies has formed retail health clinic and online chronic disease mode.It is recommended that Chinese government should issue measures and supporting guidelines for the management of chronic diseases in community pharmacies as soon as possible.Community pharmacies should be encouraged to carry out chronic disease management with the concept of prudent inclusion and gradual progression.Meanwhile,the concentration of drug retail industry should be improved to carry out the systematic construction of chronic disease management and build a standardized chronic disease service process.Besides,community pharmacies should make full use of new technologies such as the Internet,cloud computing and big data,smart wearable devices,and chronic disease management Apps to explore and carry out online professional chronic disease management mode.

Analyzing and Designing Business Processes Management System Based on Transformation of Instantiated Ontology’s State

The paper discusses efforts of finding a simple and transparent method to analyze business processes and developing its management system architecture. By introducing ontology and its state, the approach tried to find a unified representation and a flexible choreography of business processes. The main idea of the paper is the transformation of ontology＇s states, which are the most important scenarios of enterprises. The business activity composition, that is, case composition based on an AI technique, Case Based Reason （CBR）, which is to solve new problems by retrieving solutions to previous problems, and then store the modified solution. The main interest in CBR relies on that it allows a system to avoid past failures and exploit past successes.

State of Health Estimation of LiFePO_(4) Batteries for Battery Management Systems

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

Application of Power Electronics and Control for Dual Battery Packs Management with Voltage Balancing and State of Charge Estimation

Energy storage, such as lead acid batteries, is necessary for renewable energy sources’ autonomy because of their intermittent nature, which makes them more frequently used than traditional energy sources to reduce operating costs. The battery storage system has to be monitored and managed to prevent serious problems such as battery overcharging, over-discharging, overheating, battery unbalancing, thermal runaway, and fire dangers. For voltage balancing between batteries in the pack throughout the charging period and the SOC estimate, a modified lossless switching mechanism is used in this research’s suggested battery management system. The OCV state of charge calculation, in the beginning, was used in conjunction with the coulomb counting approach to estimate the SOC. The results reveal that correlation factor K has an average value of 0.3 volts when VM ≥ 12 V and an average value of 0.825 when VM ≤ 12 V. The battery monitoring system revealed that voltage balancing was accomplished during the charging process in park one after 80 seconds with a SOC difference of 1.4% between Batteries 1 and 2. On the other hand, the system estimates the state of charge during the discharging process in two packs, with a maximum DOD of 10.8 V for all batteries. The project’s objectives were met since the BMS estimated SOC and achieved voltage balance.

Vehicle Fleet Management Practices and Service Delivery in State Owned Enterprises in Zimbabwe

The main objective of the study was to examine the influence of vehicle fleet management practices on service delivery in State-Owned Enterprises (SOEs) in Zimbabwe. The study adopted a pragmatism research philosophy together with a mixed method research paradigm. In addition, structured questionnaires were distributed to 344 respondents drawn from 86 SOEs. Stratified and purposive sampling was used. Descriptive statistics were calculated using Statistical Package for the Social Science (SPSS) version 20. Exploratory factor analysis (EFA) was done on all items of the study while research hypotheses were tested using Structural Equation Modelling (SEM) in AMOS version 21. The study concluded that vehicle maintenance, fuel management, driver management and vehicle replacement positively influence service delivery. In addition, the study also established that Information and Communication Technologies (ICTs) moderate the influence of vehicle fleet management practices on service delivery. The study recommended that there is need for regular driver training and vehicle programs encompassing electronic spares tracking.

30 years of reform of China's state-owned asset management system

Reform of China’s state-owned asset management system is an important component of China’s economic system reform,but also a key factor in rejuvenating the national economy.In this article,the authors analyzed the background of the reform,summarized the reform process and discussed related question on how to deepen the reform.

Toward sustainable waste management in small islands developing states: integrated waste-to-energy solutions in Maldives context

Effective waste management is a major challenge for Small Island Developing States (SIDS) like Maldives due to limited land availability. Maldives exemplifies these issues as one of the most geographically dispersed countries, with a population unevenly distributed across numerous islands varying greatly in size and population density. This study provides an in-depth analysis of the unique waste management practices across different regions of Maldives in relation to its natural and socioeconomic context. Data shows Maldives has one of the highest population density and per capita waste generation among SIDS, despite its small land area and medium GDP per capita. Large disparities exist between the densely populated capital Male’ with only 5.8 km2 area generating 63% of waste and the ~194 scattered outer islands with ad hoc waste management practices. Given Male’s dense population and high calorific waste, incineration could generate up to ~30 GW/a energy and even increase Maldives’ renewable energy supply by 200%. In contrast, decentralized anaerobic digestion presents an optimal solution for outer islands to reduce waste volume while providing over 40%–100% energy supply for daily cooking in local families. This timely study delivers valuable insights into designing context-specific waste-to-energy systems and integrated waste policies tailored to Maldives’ distinct regions. The framework presented can also guide other SIDS facing similar challenges as Maldives in establishing sustainable, ecologically sound waste management strategies.

Deep learning-based battery state of charge estimation:Enhancing estimation performance with unlabelled training samples

The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.

Boosting battery state of health estimation based on self-supervised learning

State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

Softw are Maintainability Evaluation Based on Software Configuration Management Data

As one of the most important attributes of software quality, software maintainability has been widely recognized.However,the existing maintainability evaluation methods are mostly based on subjectively judgment. Thus it is inapplicable or unbelievable. To evaluate software maintainability objectively,the software configuration management( SCM) data are collected to establish a maintainability model. Based on the hidden Markov chain( HMC), a three-state maintainability estimation model is constructed. To validate the feasibility of the model,a real software example of software maintenance activity is given and the result from the example shows the effectiveness of the proposed method.

Intelligent Evidence-Based Management for Data Collection and Decision-Making Using Algorithmic Randomness and Active Learning

We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.

Management Procedures for Data Collected via Intelligent Transportation Systems

Addressing transportation planning, operation and investment challenges requires increasingly sophisticated data and information management strategies. ITS （intelligent transportation systems） and CV （connected vehicle） technologies represent a new approach to capturing and using needed transportation data in real time or near real time. In the case of Michigan, several ITS programs have been launched successfully, but independently of each other. The objective of this research is to evaluate and assess all important factors that will influence the collection, management and use of ITS data, and recommend strategies to develop integrated, dynamic and adaptive data management systems for state transportation agencies.

Three Management Innovations of SGCC and Three Transformations of IBM

Through analyses of the three transformations of IBM and the three management innovations of the State Grid Corporation of China (SGCC) and its consequential achievements, this paper condudes that the key to management is innovation. An enterprise can obtain core competitive advantages for sustainable development only if the innovation it makes is closely integrated with its management. Hereby SGCC has set up a good example for other state-owned enterprises to carry out innovational management.