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.

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.

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.

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.

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.

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.

Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target values.However,improving predictive accuracy is a crucial step for informed decision-making.In the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis.Among ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification tasks.The network weights and the activation functions are the two crucial elements in the learning process of an ANN.These weights affect the prediction ability and the convergence efficiency of the network.In traditional settings,ANNs assign random weights to the inputs.This research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random weights.The proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer classification.This system computes the confusion matrix-based metrics for traditional and proposed frameworks.The proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other models.The results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research limitations.Moreover,the proposed framework is of use to predict and classify cancer patients accurately.Consequently,this will facilitate the effective management of cancer patients.

Marine oil spill contingency planning

According to the practice researching and formulating “The Oil Spill Contingency Plan of South Chinese Sea”, this paper analyses and discusses the structure, functions and main contents of marine oil spill contingency planning, programs the organizing and commanding system and emergency response system, and advances the planning and researching method to coordinate comprehensively and to design practically the detailed emergency response steps until to formulate the ease operating programs for the plan implementation(PPI) and the PPI to apply high techniques supporting emergency administrations and response.

From Parallel Plants to Smart Plants:Intelligent Control and Management for Plant Growth

Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description U+0028 the crop model U+0029, prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed. © 2017 Chinese Association of Automation.

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）.

Big Data for Precision Medicine

This article focuses on the potential impact of big data analysis to improve health, prevent and detect disease at an earlier stage, and personalize interventions. The role that big data analytics may have in interrogating the patient electronic health record toward improved clinical decision support is discussed. Weexamine developments in pharmacogenetics that have increased our appreciation of the reasons why patients respond differently to chemotherapy. We also assess the expansion of online health communications and the way in which this data may be capitalized on in order to detect public health threats and control or contain epidemics. Finally, we describe how a new generation of wearable and implantable body sensors may improve wellbeing, streamline management of chronic diseases, and improve the quality of surgical implants.

Novel D-galactosamine-induced cynomolgus monkey model of acute liver failure

AIM To establish a simplified, reproducible D-galactosamineinduced cynomolgus monkey model of acute liver failure having an appropriate treatment window. METHODS Sixteen cynomolgus monkeys were randomly dividedinto four groups(A, B, C and D) after intracranial pressure(ICP) sensor implantation. D-galactosamine at 0.3, 0.25, 0.20 + 0.05(24 h interval), and 0.20 g/kg body weight, respectively, was injected via the small saphenous vein. Vital signs, ICP, biochemical indices, and inflammatory factors were recorded at 0, 12, 24, 36, 48, 72, 96, and 120 h after D-galactosamine administration. Progression of clinical manifestations, survival times, and results of H&E staining, TUNEL, and Masson staining were recorded. RESULTS Cynomolgus monkeys developed different degrees of debilitation, loss of appetite, and jaundice after D-galactosamine administration. Survival times of groups A, B, and C were 56 ± 8.7 h, 95 ± 5.5 h, and 99 ± 2.2 h, respectively, and in group D all monkeys survived the 144-h observation period except for one, which died at 136 h. Blood levels of ALT, AST, CK, LDH, TBi L, Cr, BUN, and ammonia, prothrombin time, ICP, endotoxin, and inflammatory markers [(tumor necrosis factor(TNF)-α, interleukin(IL)-1β, and IL-6)] significantly increased compared with baseline values in different groups(P < 0.05). Pathological results showed obvious liver cell necrosis that was positively correlated with the dose of D-galactosamine.CONCLUSION We successfully established a simplified, reproducible D-galactosamine-induced cynomolgus monkey model of acute liver failure, and the single or divided dosage of 0.25 g/kg is optimal for creating this model.

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).