The Support Systems and Coping Strategies of Infertile Women Attending the Out-Patient Consultation Unit of CHRACERH Yaoundé, Cameroon: A Cross-Sectional Study

Background: Infertility is a complex disorder with significant psycho-social and economic consequences. It globally affects 10% - 15% of couples. In Cameroon, little is known about what women do to overcome the psychosocial aspects of the disease. Objectives: This study aimed to identify the support systems and coping strategies of infertile women attending the outpatient consultation unit of the Gynaecological Endoscopic Surgery and Reproductive Teaching Hospital (CHRACERH), Yaoundé, Cameroon. Methods: A hospital-based cross-sectional study was conducted from the 14th of March to the 6th of April 2023 at CHRACERH Yaoundé. A total of 190 participants were recruited using a convenience sampling method. Data regarding socio-demographic characteristics, support systems and coping strategies were collected using a pretested questionnaire. Descriptive and analytic statistics were conducted using SPSS version 25. Results: The mean age of participants was 39.52 ± 7.64 years. The majority 78.9% of participants were workers (public, private sector, or traders) and were Christians 95.8%. The most common source of psychological support was from family 76.8 and husbands 72.63%. Most of the participants 89.5% resorted to prayer and getting busy 48.4% as a coping strategy. There was no statistically significant relationship between coping strategies and psychological disorders p > 0.05. Conclusion: The main support system of participants was family, husband, and friends. Prayer, getting busy and adoption were the most common coping strategies. There is a need for the Ministry of Public Health and other stakeholders to put in place other support systems and coping strategies (FELICIA) used elsewhere and provide adequate health education and infection control to prevent infertility in Cameroon.

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more water that would have otherwise been used to open more land and be used in other water-requiring sectors. Various studies suggest Alternate Wetting and Drying (AWD) as an alternative practice for water management that reduces water use without significantly affecting yield. However, this practice has not been well adopted by the farmers despite its significant benefits of reduced total water use. Improving the adoption of AWD using irrigation Decision Support Systems (DSSs) helps the farmer on two fronts;to know “how much water to apply” and “when to irrigate”, which is very critical in maximizing productivity. This paper reviews the applicability of DSSs using AWD in lowland rice production systems in Sub-Saharan Africa.

Progress of clinical decision support systems in stroke nursing care

Stroke is characterized by high incidence,high recurrence,high disability,and high morbidity and mortality in China,resulting in a heavy social and clinical burden.A clinical decision support system,as an intelli-gent computer system,can assist nurses in decision-mak-ing to collect information quickly,make the most suitable personalized decisions for patients,and improve nurses’decision-making judgment and quality of care.Promoting the development and application of decision support sys-tems in stroke nursing significantly enhances the nursing staff’s work quality and patients’prognosis.Therefore,this paper reviews the research progress of domestic and international clinical decision support systems in stroke nursing care to provide other researchers with specific research directions for developing and applying decision support systems in stroke nursing care.

Mechanism of wet shotcrete interacting with rock in support systems

In order to have a good understanding of the behavior of wet shotcrete as a support element interacting with the rock mass,mechanism of wet shotcrete interacting with rock in support systems was analyzed through theoretical,numerical study and analytical analysis.A new model of distribution of rock stress state after wet shotcrete was applied,which includes shotcrete layer,composite layer,strengthening layer,plastic layer and elastic layer,and a full illustration of the rock mass stress state was given after shotcrete interacting with rock mass.At the same time,numerical analysis with FLAC gives a stress distribution along the monitor line,respectively,at the sidewall and roof of the tunnel.The displacement obviously decreases with the depth of rock,the tangential stress for tunnel supported by shotcrete is lower than that without shotcrete,and radial stress for tunnel supported by shotcrete is higher than that without shotcrete.It has been demonstrated by AIRY'S stress function,which gives a reasonable solution.Finally,the application of wet shotcrete in Jinfeng Gold Mine shows that the displacement of tunnel decreases obviously in sidewall and roof.

Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires

Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natural and cultural environments, economies, and the life quality of local and regional populations. Thus, the selection of strategies to manage forest fires, while considering both functional and economic efficiency, is of primary importance. The use of decision support systems(DSSs) by managers of forest fires has rapidly increased. This has strengthened capacity to prevent and suppress forest fires while protecting human lives and property. DSSs are a tool that can benefit incident management and decision making and policy, especially for emergencies such as natural disasters. In this study we reviewed state-of-the-art DSSs that use: database management systems and mathematical/economic algorithms for spatial optimization of firefighting forces; forest fire simulators and satellite technology for immediate detection and prediction of evolution of forest fires; GIS platforms that incorporate several tools to manipulate, process and analyze geographic data and develop strategic and operational plans.

Group Support Systems: Implications for Multi-Disciplinary Theory Building

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Electronic market models for decision support systems on the Web

With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). We propose a formal definition and a conceptual framework for Web-based open DSS (WODSS). The formal definition gives an overall view of WODSS, and the conceptual framework based on browser/broker/server computing mode employs the electronic market to mediate decision-makers and providers, and facilitate sharing and reusing of decision resources. We also develop an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that drive WODSS operate efficiently.

Operations Support Systems Standards:Status Quo and Trends

The emerging of diversified new telecommunications technologies leads to a continuous change of telecom networks. Consequently, the operations support systems of telecommunications operators are facing structure adjustments as well as new systems construction. In this situation, new generation operations support systems standards are urgently required. Several standardization organizations have made substantial progress in the study of the new generation standards, such as ITU' s study on Next Generation Network (NGN) management, TMF's on New Generation Operations Systems and Software (NGOSS) and CCSA's on network management standards. However, the existing operations support systems face the challenges of architecture improvement, change of the focus of operations support, orientation of customers' demands and technology evolution.

Key Technology Analysis for Driver Support Systems in Japan

This paper presents the technical survey and the trend analysis of the driver support technologies such as a pre-crush braking system in Japan. In the first part, Vehicle Intelligence to assist drivers is defined by two objective functions which are both TGA （Target Generation Agent） and TAA （Target Accomplishment Agent）. TAA is mainly based on the conventional technologies that are braking smoothly, or driving with lower fuel consumption. On the other hand, TGA has the intelligent function instead of human drivers. The actual TGA are explained using some concrete driver support systems. After that, Japanese market introduction date and evolution of driver support systems are discussed with clarifying cognitive aspects which are the perception support, the judgment support and the execution support. And Key technologies underlying evolution of driver support systems are explained. Finally the author concludes that the knowledge and insights needed for intelligent driver support systems will be much more complex than in the case of autonomous vehicles that drive themselves.

Bibliometrics analysis of clinical decision support systems research in nursing

Objective:Artificial intelligence(AI)has a big impact on healthcare now and in the future.Nurses play an important role in the medical field and will benefit greatly from this technology.AI-Enabled Clinical Decision Support Systems have received a great deal of attention recently.Bibliometric analysis can offer an objective,systematic,and comprehensive analysis of a specific field with a vast background.However,no bibliometric analysis has investigated AI-enabled clinical decision support systems research in nursing.The purpose of research to determine the characteristics of articles about the global performance and development of AI-enabled clinical decision support systems research in nursing.Methods:In this study,the bibliometric approach was used to estimate the searched data on clinical decision support systems research in nursing from 2009 to 2022,and we also utilized CiteSpace and VOSviewer software to build visualizing maps to assess the contribution of different journals,authors,et al.,as well as to identify research hot spots and promising future trends in this research field.Result:From 2009 to 2022,a total of 2,159 publications were retrieved.The number of publications and citations on AI-enabled clinical decision support systems research in nursing has increased obvious ly in recent years.However,they are understudied in the field of nursing and there is a compelling need to develop more high-quality research.Conclusion:AI-Enabled Nursing Decision Support System use in clinical practice is still in its early stages.These analyses and results hope to provide useful information and references for future research directions for researchers and nursing practitioners who use AI-enabled clinical decision support systems.

Intervention Research and Support Systems for People Affected by Cancer and Their Families:Results of a Descriptive Analysis

Population health intervention research(PHIR)was initiated in the field of primary prevention by proposing a research paradigm focusing on intervention and the theory of solutions.The intervention was coconstructed with the stakeholders as part of a global approach until its deployment in the local area.The development of PHIR raises the question of its application to tertiary prevention.This study proposes some initial thoughts on the similarities and specificities of PHIR projects-funded by the French National Cancer Institute(INCa)-of support systems for people affected by cancer and their families,which were based on a descriptive analysis.The selected projects were obtained from the databases of PHIR projects funded from 2010 to 2021,which included 72 projects to which the inclusion and exclusion criteria were applied.A descriptive analysis was carried out for each project using the application files and the interim or final reports sent to INCa.A total of 30 projects were selected,half of which were in the public health field.Almost all of the projects involved a university–hospital partnership,and one-third involved healthcare professionals and patients in the research process.Patient involvement was classified into four types,which were linked to the supporting structure and the target population.The main themes of the projects were quality of life and adapted physical activity,which were assessed in hospitals through randomized controlled trials targeting specific cancers.Research has revealed several specificities and similarities between support systems for people affected by cancer and their families developed in PHIR projects;however,these support systems vary widely,and PHIR in tertiary prevention can be conducted in several ways.Any modeling of the systems is complicated;PHIR is evolving in its application and is the subject of research and evaluation.

Gap analysis in decision support systems for real-estate in the era of the digital earth

Searching for a property is inherently a multicriteria spatial decision.The decision is primarily based on three high-level criteria composed of household needs,building facilities,and location characteristics.Location choice is driven by diverse characteristics;including but not limited to environmental factors,access,services,and the socioeconomic status of a neighbourhood.This article aims to identify the gap between theory and practice in presenting information on location choice by using a gap analysis methodology through the development of a sevenfactor classification tool and an assessment of international property websites.Despite the availability of digital earth data,the results suggest that real-estate websites are poor at providing sufficient location information to support efficient spatial decision making.Based on a case study in Dublin,Ireland,we find that although neighbourhood digital earth data may be readily available to support decision making,the gap persists.We hypothesise that the reason is two-fold.Firstly,there is a technical challenge to transform location data into usable information.Secondly,the market may not wish to provide location information which can be perceived as negative.We conclude this article with a discussion of critical issues necessary for designing a spatial decision support system for real-estate decision making.

Supporting farmer-managed irrigation systems in the Shigar valley, Karakorum： Role of the government and Aga Khan Rural Support Programme

Farmer-managed irrigation systems(FMIS) in the high altitude valleys of the Karakorum,Pakistan, continue to be managed effectively despite increased pressure on the social arrangements that sustain them. Colonial era records shows that over a century ago government agencies undertook irrigation support projects. In the past three decades,government agencies and the non-government agency Aga Khan Rural Support Programme(AKRSP), which channels foreign funds into the region, have actively engaged in the provision of irrigation support. This article seeks to explore whether such projects support or undermine farmer-managed irrigation systems and the complex institutional arrangements that underpin them. Field research using ethnographic and participatory methods was conducted in spring 2013 in the upper Shigar valley, Skardu district, GilgitBaltistan. The findings show that irrigation development is a political activity that involves village-based actors, religious leaders, local politicians,and government and non-government agencies.Government agencies operate in a largely top-down,engineering mode, their larger projects limited to villages suffering water scarcity. The local government provides small funds for renovation work of FMIS,though allocation of funds is highly politicized. Nongovernment agencies, for a variety of reasons including donor-funding cycles, apply a one-size-fitsall ‘participatory' model in an attempt to socially engineer rules and institutions. In communities divided by factionalism the use of such external models that stress formation of committees are unlikely to yield positive results, and could instead contribute to undermining the very systems they seek to support. This research argues that irrigation interventions should take care to build upon the rich and complex social arrangements that have sustained FMIS through the centuries.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

Detection of PERV by polymerase chain reaction and its safety in bioartificial liver support system

AIM： To establish a method detecting porcine endogenous retrovirus （PERV） in China experimental minipigs and to evaluate the safety of PERV in three individuals treated with bioartificial liver support systems based on porcine hepatocytes. METHODS： Porcine hepatocytes were isolated with two-stage perfusion method, then cultured in the bioreactor, which is separated by a semipermeable membrane （0.2μm） from the lumen through which the patients＇ blood plasma was circulated. After posthemoperfusion, patients＇ blood was obtained for screening. Additionally, samples of medium collected from both intraluminal and extraluminal compartments of the laboratory bioreactor and culture supernate in vitro was analyzed. The presence of viral sequences was estimated by polymerase chain reaction （PCR） and reverse transcriptase-polymerase chain reaction （RTPCR）. Finally, the infection of virus in the supernate of common culture was ascertained by exposure to the fetal liver cells. RESULTS： PERV-specific gag sequences were found in the porcine hepatocytes using RT-PCR. and were detected in all samples from the intraluminal, extraluminal samples and culture supernate. However, culture supernatant from primary porcine hepatocytes （cleared of cellular debris） failed to infect human fetal liver cells. Finally, RT-PCR detected no PERV infection was found in the blood samples obtained from three patients at various times post-hemoperfusion. CONCLUSION： The assays used are specific and sensitive, identified by second PCR. PERVs could be released from hepatocytes cultured in bioreactor without the stimulation of mitogen and could not be prevented by the hollow fiber semipermeable membrane, indicating the existence of PERV safety in extracorporeal bioartificial liver support system （EBLSS）.

A Simulation-Based Decision Support System for Manufacturing Enterprise

The simulation-based decision support system (SBDSS) is designed to achieve a highlevel of performance, flexibility and adaptability, in response to meet the special needs of productionand logistics management during the economic system reform era in China. It consists two subsys-tems: the object library modeler (OLM) and the simulation engine and its manager (SEM). UsingSBDSS the decision makers can work out their optimal production choice under certain circumstancesthrough scenario simulations. And they can test a set of virtual organizations reflecting systems re-form before a real reorganization has been taken, as well as perform a virtual manufacturing processfor a new product design (Copyright @ 1998 IFAC).

Analysis and Study of Parallel Processing Mode inVLDB Decision Support System

Nowadays, many kinds of computer network data management systems have been built widely in China. People have realized widely that management information system (MIS) has brought a revolution to the management mechanism. Moreover, the managers of company need wide-range and comprehensive decision information more and more urgently which is the character of information explosion era. The needs of users become harsher and harsher in the design of MIS, and these needs have brought new problems to the general designers of MIS. Furthermore, the current method of traditional database development can't solve so big and complex problems of wide-range and comprehensive information processing. This paper proposes the adoption of parallel processing mode, the built of new decision support system (DSS) is to discuss and analyze the problems of information collection, processing and the acquirement of full-merit information with cross-domain and cross-VLDB (very-large database).

Nursing decision support system:application in electronic health records

The clinical decision support system makes electronic health records(EHRs)structured,intelligent,and knowledgeable.The nursing decision support system(NDSS)is based on clinical nursing guidelines and nursing process to provide intelligent suggestions and reminders.The impact on nurses’work is mainly in shortening the recording time,improving the quality of nursing diagnosis,reducing the incidence of nursing risk events,and so on.However,there is no authoritative standard for the NDSS at home and abroad.This review introduces development and challenges of EHRs and recommends the application of the NDSS in EHRs,namely the nursing assessment decision support system,the nursing diagnostic decision support system,and the nursing care planning decision support system(including nursing intervene),hoping to provide a new thought and method to structure impeccable EHRs.

Reformative Financial Risk Management Approach： A Multistage Decision Support System with the Assistance of Fuzzy Goal Programming and Expertons Method

The potential demand on financial risk management has being increased considerably by the reason of Basel 11 regulations and instabilities in economy. In recent years, financial institutions and companies have been struggled for building up intensive financial risk management tools due to Basel II guidance on establishing financial self-assessment systems. In this respect, decision support system has a significant role on effectuating intensive financial risk management roadmap. In this study, a reformative financial risk management system is presented with the combination of determining financial risks with their importance, calculating risk scores and making suggestions based on detected risk scores by applying corrective actions. First, financial risk factors and indicators of these risk variables are selected and weights of these variables are specified by using fuzzy goal programming. After that, total risk scores are calculated and amendatory financial activities are appeared by means of expertons method which also provides possibilities of the alternative decisions. To illustrate the performance of integrated and multistage decision support system, a survey is applied on the end users.

A Decision Support Model for Predicting Avoidable Re-Hospitalization of Breast Cancer Patients in Kenyatta National Hospital

This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.