Adaptive Information Research Articles

Adaptive fractional physical information neural network based on PQI scheme for solving time-fractional partial differential equations

<abstract> <p>In this paper, an accurate fractional physical information neural network with an adaptive learning rate (adaptive-fPINN-PQI) was first proposed for solving fractional partial differential equations. First, piecewise quadratic interpolation (PQI) in the sense of the Hadamard finite-part integral was introduced in the neural network to discretize the time-fractional derivative in the Caputo sense. Second, the adaptive learning rate residual network was constructed to keep the network from being stuck in the locally optimal solution, which automatically adjusts the weights of different loss terms, significantly balancing their gradients. Additionally, different from the traditional physical information neural networks, this neural network employs a new composite activation function based on the principle of Fourier transform instead of a single activation function, which significantly enhances the network's accuracy. Finally, numerous time-fractional diffusion and time-fractional phase-field equations were solved using the proposed adaptive-fPINN-PQI to demonstrate its high precision and efficiency.</p> </abstract>

Research on cross-modal emotion recognition based on multi-layer semantic fusion.

Multimodal emotion analysis involves the integration of information from various modalities to better understand human emotions. In this paper, we propose the Cross-modal Emotion Recognition based on multi-layer semantic fusion (CM-MSF) model, which aims to leverage the complementarity of important information between modalities and extract advanced features in an adaptive manner. To achieve comprehensive and rich feature extraction from multimodal sources, considering different dimensions and depth levels, we design a parallel deep learning algorithm module that focuses on extracting features from individual modalities, ensuring cost-effective alignment of extracted features. Furthermore, a cascaded cross-modal encoder module based on Bidirectional Long Short-Term Memory (BILSTM) layer and Convolutional 1D (ConV1d) is introduced to facilitate inter-modal information complementation. This module enables the seamless integration of information across modalities, effectively addressing the challenges associated with signal heterogeneity. To facilitate flexible and adaptive information selection and delivery, we design the Mask-gated Fusion Networks (MGF-module), which combines masking technology with gating structures. This approach allows for precise control over the information flow of each modality through gating vectors, mitigating issues related to low recognition accuracy and emotional misjudgment caused by complex features and noisy redundant information. The CM-MSF model underwent evaluation using the widely recognized multimodal emotion recognition datasets CMU-MOSI and CMU-MOSEI. The experimental findings illustrate the exceptional performance of the model, with binary classification accuracies of 89.1% and 88.6%, as well as F1 scores of 87.9% and 88.1% on the CMU-MOSI and CMU-MOSEI datasets, respectively. These results unequivocally validate the effectiveness of our approach in accurately recognizing and classifying emotions.

Eye Movement Desensitization Reprocessing Therapy in Case of Anxiety in Teenager: A Case Report

ABSTRACT Anxiety and its related issues are among the most common mental health concerns affecting adolescents worldwide. Adolescence is a period marked by significant physical, emotional, and social changes, making individuals particularly vulnerable to the development of performance anxiety especially with academics. This case report aims to show the effectiveness of using Eye Movement Desensitization Reprocessing (EMDR), a trauma focused model of psychotherapy in treating anxiety-related issues in a female child of 17 years old. The report presents a detailed review of EMDR intervention which includes the Three three-pronged Adaptive Information Processing (AIP) model and the eight phases of EMDR therapy. The noticeable reduction in the client’s anxiety, fear, improved speed of writing and focus affirms the effectiveness of EMDR as an intervention in addressing the anxiety associated with past traumatic memory. The case study brought fourth how use of Assertiveness training and Cognitive Behaviour Techniques along with EMDR enhanced the overall wellbeing of the client.

Across Sessions and Subjects Domain Adaptation for Building Robust Myoelectric Interface.

Gesture interaction via surface electromyography (sEMG) signal is a promising approach for advanced human-computer interaction systems. However, improving the performance of the myoelectric interface is challenging due to the domain shift caused by the signal's inherent variability. To enhance the interface's robustness, we propose a novel adaptive information fusion neural network (AIFNN) framework, which could effectively reduce the effects of multiple scenarios. Specifically, domain adversarial training is established to inhibit the shared network's weights from exploiting domain-specific representation, thus allowing for the extraction of domain-invariant features. Effectively, classification loss, domain diversence loss and domain discrimination loss are employed, which improve classification performance while reduce distribution mismatches between the two domains. To simulate the application of myoelectric interface, experiments were carried out involving three scenarios (intra-session, inter-session and inter-subject scenarios). Ten non-disabled subjects were recruited to perform sixteen gestures for ten consecutive days. The experimental results indicated that the performance of AIFNN was better than two other state-of-the-art transfer learning approaches, namely fine-tuning (FT) and domain adversarial network (DANN). This study demonstrates the capability of AIFNN to maintain robustness over time and generalize across users in practical myoelectric interface implementations. These findings could serve as a foundation for future deployments.

Breaking barriers in accessing and uptaking climate change adaptation information by smallholder tomato farmers in Tanzania

Purpose This study aims to assess barriers in accessing and uptaking climate change adaptation information among smallholder tomato farmers in Tanzania. Design/methodology/approach This study was conducted in Iringa and Morogoro regions, Tanzania. It adopted cross-sectional research design. Multistage, purposive and random sampling techniques with a sample size of 183 smallholder tomato farmers were used. Questionnaires containing closed- and open-ended questions, interview guide and focus group discussions were used to collect data. To ensure validity and reliability of data collection instruments, pretesting of the questionnaires was conducted by administering them to a small group of smallholder tomato growers. Statistical Product and Service Solutions software was used to analyse quantitative data while NVivo was used for analysing qualitative data. Findings Lack of clear understanding of climate change information (132, 72.1%), scarcity of libraries and information centres (125, 68.3%), inadequate income (125, 68.3%), lack of trust of sources of information (114, 62.3%), lack of sufficient knowledge about climate change (111, 60%), illiteracy (110, 60.1%), poor distribution of electricity in rural areas (109, 59.6%) and use of jargons (93, 50.8%) in explaining climate change adaptation issues are hindrances for smallholder tomato farmers to access and uptake climate change adaptation information. Research limitations/implications This study was conducted in only two districts in Tanzania; thus, the findings cannot be generalized. Practical implications The government and relevant agencies are recommended to use appropriate strategies for breaking the barriers that impede easy access and uptake of climate change adaptation information by smallholder tomato farmers as one of the interventions to combat the impacts posed by climate change. Social implications This study has contributed to the ongoing discourses on climate change issues. Such discourses have influenced attention and understanding of the role of access and uptake of climate change adaptation information by smallholder farmers for planning and implementation of adaptation strategies through awareness creation. Originality/value This study is in line with goal number 13.3 of Sustainable Development Goals, which vows on taking necessary actions in combating climate change and its impacts. Thus, smallholder tomato farmers’ awareness about climate change through access and uptake of climate change adaptation information is one of the interventions to combat climate change and its impacts.

Mechanistic modeling of alarm signaling in seed-harvester ants.

Ant colonies demonstrate a finely tuned alarm response to potential threats, offering a uniquely manageable empirical setting for exploring adaptive information diffusion within groups. To effectively address potential dangers, a social group must swiftly communicate the threat throughout the collective while conserving energy in the event that the threat is unfounded. Through a combination of modeling, simulation, and empirical observations of alarm spread and damping patterns, we identified the behavioral rules governing this adaptive response. Experimental trials involving alarmed ant workers (Pogonomyrmex californicus) released into a tranquil group of nestmates revealed a consistent pattern of rapid alarm propagation followed by a comparatively extended decay period [1]. The experiments in [1] showed that individual ants exhibiting alarm behavior increased their movement speed, with variations in response to alarm stimuli, particularly during the peak of the reaction. We used the data in [1] to investigate whether these observed characteristics alone could account for the swift mobility increase and gradual decay of alarm excitement. Our self-propelled particle model incorporated a switch-like mechanism for ants' response to alarm signals and individual variations in the intensity of speed increased after encountering these signals. This study aligned with the established hypothesis that individual ants possess cognitive abilities to process and disseminate information, contributing to collective cognition within the colony (see [2] and the references therein). The elements examined in this research support this hypothesis by reproducing statistical features of the empirical speed distribution across various parameter values.

Establishing a national indicator-based surveillance system for hospital bed utilization by COVID-19 patients in the Philippines.

In March 2020, the Philippine Department of Health (DOH) designed and rapidly implemented a national surveillance system for the utilization of hospital beds by patients with coronavirus disease (COVID-19) to produce complete and timely data for use by various levels of governance in response to the COVID-19 pandemic. The DOH launched the DOH DataCollect (DDC) Bed Tracker system, a web-based application that collects information from all 1906 public and private hospitals and infirmaries across the country using a modular data collection tool. Data on the maximum number of occupied COVID-19-designated beds (n=28 261), hospital bed utilization rate (71.7%), and mechanical ventilator number (n=1846) and utilization rate (58.5%) were recorded in September 2021 during the Delta surge of cases in the Philippines. Data on human resources, personal protective equipment and supplies, and other operational indicators were added to the system during various modifications. Information from the DDC was used to inform the COVID-19 response and operations at national and local levels and facilitated research at academic and nongovernmental agencies. The development of the DDC system demonstrates that an effective surveillance system for use by all health-care facilities is achievable through strong national leadership, the use of available technology and adaptive information systems, and the establishment of networks across different health facilities and stakeholders.

МЕТОД АДАПТИВНОГО БАГАТОШАРОВОГО ЗАХИСТУ ІНФОРМАЦІЇ НА ОСНОВІ СТЕГАНОГРАФІЇ ТА КРИПТОГРАФІЇ

An analysis of known solutions to the problem of steganographic and cryptographic methods combining to achieve multilayer information protection is presented. Results of the analysis allowed us to identify trends and prospects for the development of these kink of methods. Based on the results of the analysis, the research task was determined concerning the adaptation of the used cryptographic and steganographic methods in order to achieve the best level of protection, which is needed at critical systems. Metrics were defined, those allowed to choose the best combination of cryptographic and steganographic protection methods parameters. A method of multilayered information protection is presented, which combines cryptographic and steganographic approaches to ensure an increased level of information’s confidentiality and integrity protection. It is proposed to introduce criteria for the selection of cryptographic transformations in such a way that their combination together with steganographic transformations gives the best impact. An instance of the proposed method implementation is given in order to prove the concept. An algorithm has been developed that implements the proposed method of adaptive multilayer information protection. The perspective of further research is determined.

ESTIMATION OF PROBABILISTIC AND STATISTICAL CHARACTERISTICS OF THE INFORMATIONAL STATE OF A COMMUNICATION CHANNEL

The methods for determining the informational state of a communication channel based on the assessment of probabilistic and statistical characteristics of errors at the channel output are presented. Adaptive information protection methods in digital communication systems, where encoder parameters change depending on the channel state, require the use of network quality control methods based on determining the statistical characteristics of the channel. It is shown that the bit error probability in the channel is constant for the presented error generation process and is independent of the method of dividing information into code blocks. The methods for assessing the statistical characteristics of the channel are based on the topological representation of error generation processes over the length of code blocks. It is demonstrated that, with a fixed code sequence length and a constant bit error probability at the output of the information channel, different error distributions correspond to sets of n-sequences. With a known number of states in the likelihood transition matrix, calculation of observation intervals for counting erroneous blocks is proposed. The presented method of researching statistical characteristics, based on the assumption of error independence, allows for the development of algorithms and procedures for assessing the state of discrete communication channels. The evaluation of potential bounds and statistical characteristics for the decoding error probability enables establishing a connection between the topological characteristics of error sequence partitioning and code parameters. The work confirms that the proposed method allows the synthesis of algorithms for determining code parameters, whose numerical values are determined based on minimizing potential decoding error probabilities.

A deep learning model for multi-modal spatio-temporal irradiance forecast

Short-term spatio-temporal solar irradiance forecasting plays a pivotal role in scheduling and dispatching energy for distributed energy systems. Fluctuations in cloud cover can be monitored via satellite cloud imagery, which directly impacts irradiance. However, integrating and fusing multi-source heterogeneous data, such as satellite cloud images and ground monitoring data from distributed stations, remains challenging. Here, a spatio-temporal irradiance forecast model is proposed based on multi-modal deep learning model to predict global horizontal irradiance 30 min ahead. To address the feature extraction of heterogeneous data, a dual-channel structure consisting of a time-series processing block and a satellite cloud image processing block is developed to enable parallel processing of multi-modal features. In order To tightly couple cloud images and historical time series at the feature level, maximum mean discrepancy of these two feature is used to help the fusion of heterogeneous data. Furthermore, a self-attention mechanism is employed to construct adaptive inter-region information weights to enhance spatio-temporal representation ability. The evaluation of the method is conducted on open-access datasets from six locations in Jiangsu Province, China. Experimental results demonstrate that the proposed model efficiently utilizes heterogeneous data to improve prediction accuracy under various conditions and enhances model robustness, reducing RMSE by 2.8%–20.58%. Meanwhile, the proposed end-to-end model reduces training and deployment costs for real-world use.

Adaptive event-triggered pseudolinear consensus filter for multi-UAVs bearings-only target tracking

An improved version of the pseudolinear filter is proposed, namely an adaptive event-triggered instrumental variable-based pseudolinear information consensus filter, with a special focus on three-dimensional(3D) unbiased target tracking with multiple unmanned aerial vehicles (UAVs) bearings-only measurements under communication faults. To deal with the unknown communication faults, a credibility-based weighted average consensus rule is constructed, which considers the network topology and the information quality of UAVs. Furthermore, an adaptive event-triggered mechanism is designed to reduce the computation burden and communication bandwidth. The selective-angle-measurement strategy (SAMS)-based instrumental variable is integrated into the pseudolinear information consensus filter to achieve unbiased estimates. It is proved that the estimates of all UAVs will eventually achieve consistency, and its estimation errors are bounded. Finally, numerical simulations are given to verify the advantages of the proposed filtering algorithm in terms of enhanced accuracy and faster convergence rates.

Simultaneously Intelligent Sensing and Beamforming Based on an Adaptive Information Metasurface.

Due to its ability to adapt to a variety of electromagnetic (EM) environments, the sensing-enabled metasurface has garnered significant attention. However, large-scale EM-field sensing to obtain more information is still very challenging. Here, an adaptive information metasurface is proposed to enable intelligent sensing and wave manipulating simultaneously or more specifically, to realize intelligent target localization and beam tracking adaptively. The metasurface is composed of an array of meta-atoms, and each is loaded with two PIN diodes and a sensing-channel structure, for polarization-insensitive and programmable beamforming and sensing. By controlling the state of the PIN diode, the proposed meta-atom has 1-bit phase response in the designed frequency band, while the sensing loss keeps higher than -10dB for both "ON" and "OFF" states. Hence there is nearly no interaction between the beamforming and sensing modes. Experiments are conducted to show multiple functions of the metasurface, including intelligent target sensing and self-adaptive beamforming, and the measured results are in good agreement with the numerical simulations and theoretical calculations.

Exploration–Exploitation Tradeoff in the Adaptive Information Sampling of Unknown Spatial Fields with Mobile Robots

Adaptive information-sampling approaches enable efficient selection of mobile robots’ waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized balance the need to explore new information where the uncertainty is very high and to exploit the data sampled so far, with which a great deal of the underlying spatial fields can be obtained, such as the source locations or modalities of the physical process. However, works in the literature have either assumed the robot’s energy is unconstrained or used a homogeneous availability of energy capacity among different robots. Therefore, this paper analyzes the impact of the adaptive information-sampling algorithm’s information function used in exploration and exploitation to achieve a tradeoff between balancing the mapping, localization, and energy efficiency objectives. We use Gaussian process regression (GPR) to predict and estimate confidence bounds, thereby determining each point’s informativeness. Through extensive experimental data, we provide a deeper and holistic perspective on the effect of information function parameters on the prediction map’s accuracy (RMSE), confidence bound (variance), energy consumption (distance), and time spent (sample count) in both single- and multi-robot scenarios. The results provide meaningful insights into choosing the appropriate energy-aware information function parameters based on sensing objectives (e.g., source localization or mapping). Based on our analysis, we can conclude that it would be detrimental to give importance only to the uncertainty of the information function (which would explode the energy needs) or to the predictive mean of the information (which would jeopardize the mapping accuracy). By assigning more importance to the information uncertainly with some non-zero importance to the information value (e.g., 75:25 ratio), it is possible to achieve an optimal tradeoff between exploration and exploitation objectives while keeping the energy requirements manageable.

Improving Information Literacy in Introductory Psychology Through Short, Adaptive Interventions

Background The American Psychological Association has identified information literacy as a crucial component to undergraduate education. One major barrier to comprehensive information literacy instruction has been a lack of guidance integrating this into survey courses, particularly with reduced pedagogical resources available. Objective This research evaluates the effectiveness of a scaffolded information literacy curriculum that may be integrated into psychology courses. Method Students in introductory psychology courses were taught with either an information literacy curriculum or the typical science-focused curriculum. Information literacy knowledge was evaluated using a multiple-choice test. Results Students who received the specialized curriculum demonstrated significant improvements on an objective information literacy test by the end of the course, while students in the control group did not. Conclusion Evidence suggests that this easily adaptable information literacy curriculum is effective in meeting psychology information literacy goals, more so than the traditional science-focused curriculum taught in introductory psychology classes. Teaching Implications Instructors and faculty should be encouraged to utilize this adaptive curriculum, available on Open Science Framework, as a way of improving information literacy. Other implications, including feasibility and content, are discussed.

Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering.

Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets.

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

Abstract Complex data systems are incapable of processing large data volumes, rendering the task of retrieving pertinent information unattainable. The advent of the Internet has amplified the significance of accessible and readily available information. Additionally, it receives support from the World Wide Web Consortium (W3C) and global organizations responsible for establishing web standards, such as Web Ontology, Inc. It expands the functionality of the website to facilitate the retrieval, integration, and transmission of information. In recent years, several major organizations have shown a strong inclination towards using semantic technologies for the purpose of collecting Big Data. Undoubtedly, there are other advantages of integrating this into the Creative. It enhances the ability of end-users to manage data from many repositories, focuses on changing the corporate environment and the user experience, and incorporates individual definitions and integrates several data sources. Furthermore, the market’s evolving expectations and contemporary organizational practices require an adaptable but all-encompassing information strategy. Integration of data warehousing may be achieved by the use of scattered corporate ontologies. This study explores the impact of the Semantic Web on enhancing the intelligence of Big Data. It analyses the obstacles and opportunities associated with the integration of Big Data with the Semantic Web.

The rapid construction method of the digital twin polymorphic model for discrete manufacturing workshop

The modeling process is resource-intensive, time-consuming, and expensive due to the large number and variety of production and auxiliary equipment in discrete manufacturing plants. The popular twin-model construction method based on a single type of equipment cannot meet the demand for rapid construction and high-quality model mapping in large discrete manufacturing plants. This paper proposes a strategy for developing a digital twin polymorphic model (DTPM) for discrete manufacturing workshops to meet complex manufacturing business scenarios, improve the richness of digital twin model varieties, construction efficiency and intelligence, and form an efficient equipment model construction method. The complete element information and real-time dynamic data of the minimal component unit (Functional Components, FC) of DTPM are clustered and analyzed. In addition, properties of the functional component information model are characterized from several dimensions, such as geometry, physics, and behavior. Furthermore, the FC with highly concentrated primary components is established based on the object-oriented derivation inheritance and attribute reuse hybrid drive technique. FC drastically reduces the duration of the digital twin model development process through encapsulation, inheritance, polymorphism, and other technologies. On this premise, the DTPM construction method that deeply decouples the physical structure and functional methods is proposed. DTPM integrates adaptive information interaction technology to accomplish intelligent data connection, dynamic data updating, and digitally accurate mapping of static-dynamic-virtual multidimensional models in discrete production workshops. Lastly, the method's validity is confirmed by creating a large discrete production workplace. The results indicate that the proposed technique may significantly enhance the model variety and building efficiency of large-scale digital twin workshop systems.

Towards Adaptive Information Fusion in Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have gained great popularity in tackling various analytic tasks on graph and network data. However, some recent studies raise concerns about whether GCNs can optimally integrate node features and topological structures in a complex graph. In this paper, we first present an experimental investigation. Surprisingly, our experimental results clearly show that the capability of the state-of-the-art GCNs in fusing node features and topological structures is distant from optimal or even satisfactory. The weakness may severely hinder the capability of GCNs in some classification tasks, since GCNs may not be able to adaptively learn some deep correlation information between topological structures and node features. Can we remedy the weakness and design a new type of GCNs that can retain the advantages of the state-of-the-art GCNs and, at the same time, enhance the capability of fusing topological structures and node features substantially? We tackle the challenge and propose an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> daptive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti-channel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> onvolutional <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork for semi-supervised classification ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AM-GCN</b> ). The central idea is that we extract the specific and common embeddings from node features, topological structures, and their combinations simultaneously, and use the attention mechanism to learn adaptive importance weights of the embeddings. However, considering that the input topology and feature structure in AM-GCN are still predefined and fixed, once the properties of graph structures are not consistent with tasks, the fusion performance of AM-GCN will be hindered from the beginning. Therefore, we need to adjust the structure and further propose the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> abel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> ropagation guided <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti-channel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> onvolutional <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LPM-GCN</b> ). LPM-GCN introduces edge weights learning on both topology and feature spaces to improve structural homophily, which can better promote the fusion process of graph convolutional networks. Our extensive experiments on benchmark data sets clearly show that our proposed models extract the most correlated information from both node features and topological structures substantially, and improves the classification accuracy with a clear margin.

Integrating Compassion into EMDR for PTSD

Post-traumatic stress disorder (PTSD) often involves feeling-states of shame and self-loathing. Traditional exposure-based treatments, which are effective for fear-based PTSD, are sometimes limited in effectiveness when working with these emotional experiences: clients with high levels of shame, guilt, or disgust may experience ‘blocks’ in processing when attempting eye movement desensitization and reprocessing (EMDR). Developed to help people who suffer from pervasive shame and self-attack, compassion focused therapy (CFT) is based upon an evolutionary framework, and draws on research from human neurobiology and attachment theory to understand how humans respond to different types of threat. This paper describes a protocol for integrating compassion focused techniques into EMDR therapy with the aim of facilitating adaptive information processing. Two case examples outline the use of the protocol and describe clients’ reflections on the process. We suggest that CFT approaches can be successfully integrated with EMDR when working with experiences of shame and self-blame.

Climate change and adaptation strategies in rural Ghana: a study on smallholder farmers in the Mamprugu-Moaduri district

PurposeThis study aims to analyze the perceptions of smallholder farmers on climate change and events and further explores climate change adaptation strategies and associated challenges. The findings provide useful information for enhancing the adaptive capacity of smallholder farmers to adjust to climate-related hazards and improve their resilience and disaster preparedness in northern Ghana.Design/methodology/approachThis study uses a multistage sampling procedure and sample size of 150 farmers, the Binary Probit Model (BPM), to identify and examine the determinants of climate change adaptation strategies adopted by smallholder farmers. Also, the constraints of adaptation were analyzed using Kendall’s coefficient of concordance.FindingsThe results from the BPM and statistics of Kendall’s coefficient revealed that the farm risk level, ability to adapt, farmer’s income, age, farming experience, climate change awareness and extension visits were factors that significantly influenced the adaptation strategies of smallholder farmers (in order of importance). The majority (60%) of the farmers ranked farm risk level as the major constraint to adopting climate change strategies.Originality/valueThe findings of this study enhance understanding on access to relevant and timely climate change adaptation information such as an early warning to farmers during the start of the farming/rainy season to support their adaptive responses to climate change.