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Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

A method for approximation of the Shannon entropy of Gaussian photon‐counting processes with infinite history was constructed on the memory function of these processes, described by autoregressive‐integrated moving average (ARIMA) models. Most frequently, photon‐counting processes are stationary or nonstationary multidimensional Gaussian discrete‐time stochastic ones which justify the use of the ARIMA models. Starting from the memory function, a memory time‐equivalent finite autoregressive representation of a given process with infinite history, i.e. a stationary finite‐order Gaussian Markov chain, was determined, then corresponding autocorrelation matrices were calculated from the truncated memory function using the Yule‐Walker equations, and an autocorrelation‐based formula for approximation of the entropy of the process through the entropy of its stationary Markovian representation was given. An ARMA(1,1) process together with its stationary (MA(1)) or nonstationary (IMA(0,1,1)) boundary cases were considered to demonstrate opposite changes in the entropy as the memory time increases at a fixed variance of the process: the entropy was found to decrease for stationary processes and increase for nonstationary ones. It was also found on experimental examples (perturbed human neutrophils and yeast cells) that those changes can be reversed by opposite changes in the process variance. The method allows us to determine, at any desired accuracy, the Shannon entropy of time‐discrete stochastic processes, and reveals new aspects of the relationship between the process' stationarity, memory, entropy and heteroskedasticity.

Although the relation between individual and collective memory has been long established, analysis of individual memories is hardly existent within the social sciences outside of psychoanalysis and developmental psychology. Working to overcome this gap, the author argues that children’s lives are heavily influenced by the structures of collective memory they are born into, available to children through the complex system of inter- and intragenerational relationships from very early on.

Drawing on the concepts of generation (Karl Mannheim), generagency (Madelaine Leonard), and collective memory (Maurice Halbwachs), the author establishes that the practise of intergeneratonal exchange of memories within the family provides a way to influence and overcome the limiting of children’s agency by social stratification determined by age.

The purpose of this paper is to explore how revisiting happy and pleasant memories might bring some peace to the minds of those who struggle with the ongoing impact of early life relational trauma. The author explores previously forgotten but important memories of happier times and safe relationships which have been outweighed by other traumatic memories. The author writes about the impact of revisiting the past through a different lens and how this helped reshape and redefine the future.

The author has written about revisiting happy and pleasant memories from lived experience. The writing is rich and evocative and gives voice to previously forgotten memories of pleasant life events and how soothing this has been.

The author concludes that it has been a helpful and soothing experience to spend time recreating memories of previously happy experiences. It is noted that this brings some balance and perspective to an early life which was dominated by traumatic events. The author suggests that it is possible to lever these pleasant memories to improve self-confidence and to bring about a reduction in harsh self-criticism.

The author concludes that the stories we tell ourselves about our early life experiences impact greatly on our sense of self and the future ability to create a meaningful life moving forward. Whilst it is important for many to revisit painful experiences to process them fully and move forward, it is also important to focus on more pleasant experiences and relational contacts to bring about a fresh perspective and increased confidence. This helps to move a person from threat-centred behaviour to a more soothed and contented state.

The author has found that revisiting soothing and pleasant memories can serve to bring balance and a fresh perspective to early life experiences. It is also noted that the process of writing about these happy memoires has been beneficial in terms of successfully reliving them and savouring the helpful feelings they bring forth.

By exploring the helpfulness of revising a life that has been greatly impacted by traumatic experiences and focusing on the more pleasant and happier times, the author has shown that it is possible to think and feel differently about the past. It is also noted that it is beneficial to feel the happiness these memories bring within the body and mind, and they can bring a sense of calm. This embeds the importance of also asking trauma survivors about the times that were more pleasant and happier for them within the therapeutic process.

This is the author’s first hand and unique testimony of how helpful it was to revisit happier memories in terms of how soothing it was to focus on these amid other more problematic memories. It is noted that this helped the author to regain a sense of hope and agency in terms of moving forward. This opinion piece contains moving and evocative stories about memories of supportive and warm relationships in the life of the author. The author also notes how helpful the writing process has been in terms of slowing down and being able to fully inhabit and savour these more pleasant memories.

This paper aims to understand how mental time traveling impacts consumption by triggering nostalgia. The effects of nostalgic behavior are explored further in regards of its impact on dears and nears.

This study is based on qualitative information from in-depth interviews. In total, 30 parents with children form Chihuahua, Mexico, answer to a semi-structured interview. Participants presented nostalgic orientation.

Nostalgic individual move back and forward in time through memory retrieval. Retrieval's quality is related to social impact during memory creation and retrieval process. Nostalgia is not only a cognitive process, but it manifests on behaviors that affects people around the nostalgic individuals. In the context of parent–child relationship, sharing nostalgia is useful for creating new bond across participants.

Contributions toward theory of memory, nostalgia and social learning were made. Result suggests social implications on nostalgic behavior because social interaction is important for quality of memory retrieval. Behavioral implications are discussed in the context of parent–child relationship and the use of nostalgia to develop new and stronger bonds. Companies should develop strategies that privilege social moments around brands to increase memory retrieval quality and nostalgia.

Companies should develop strategies that create social moments around brands to increase memory retrieval quality and nostalgia. Additionally, using social moments on communications could trigger nostalgia and detonates consumption behavior.

The research builds on previous studies about nostalgia. However, this research focusses on mental time travel along nostalgic memories that individuals perform every day to take decisions that affects them and their loved ones. The value of nostalgia on building relationships through consumption is analyzed. The results were obtained from the Mexican context that has not been explored before on nostalgia research.

Internet has changed radically in the way people interact in the virtual world, in their careers or social relationships. IoT technology has added a new vision to this process by enabling connections between smart objects and humans, and also between smart objects themselves, which leads to anything, anytime, anywhere, and any media communications. IoT allows objects to physically see, hear, think, and perform tasks by making them talk to each other, share information and coordinate decisions. To enable the vision of IoT, it utilizes technologies such as ubiquitous computing, context awareness, RFID, WSN, embedded devices, CPS, communication technologies, and internet protocols. IoT is considered to be the future internet, which is significantly different from the Internet we use today. The purpose of this paper is to provide up-to-date literature on trends of IoT research which is driven by the need for convergence of several interdisciplinary technologies and new applications.

A comprehensive IoT literature review has been performed in this paper as a survey. The survey starts by providing an overview of IoT concepts, visions and evolutions. IoT architectures are also explored. Then, the most important components of IoT are discussed including a thorough discussion of IoT operating systems such as Tiny OS, Contiki OS, FreeRTOS, and RIOT. A review of IoT applications is also presented in this paper and finally, IoT challenges that can be recently encountered by researchers are introduced.

Studies of IoT literature and projects show the disproportionate importance of technology in IoT projects, which are often driven by technological interventions rather than innovation in the business model. There are a number of serious concerns about the dangers of IoT growth, particularly in the areas of privacy and security; hence, industry and government began addressing these concerns. At the end, what makes IoT exciting is that we do not yet know the exact use cases which would have the ability to significantly influence our lives.

This survey provides a comprehensive literature review on IoT techniques, operating systems and trends.

The purpose of this paper is to estimate volatility in African stock markets (ASMs), taking account of periodic level shifts in the mean level of volatility, where the regime shifts are determined endogenously.

Volatility estimates are incorporated into standard volatility models to assess the impact of structural breaks on volatility persistence, long memory and forecasting performance for ASMs.

The results presented here indeed suggest that persistence and long memory in volatility are overestimated when regime shifts are not accounted for. In particular, application of breakpoint tests and a moving average procedure suggest that unconditional volatility displays substantial time variation.

A modification of the standard generalised autoregressive conditional heteroscedasticity model to allow for time variation in the unconditional variance generates improved volatility forecasting performance for some African markets.

This paper describes one of the first studies to incorporate endogenously determined regime shifts into volatility estimates and assess the impact of structural breaks on volatility persistence, long memory and forecasting performance for ASMs.

This paper aims to explore, for the first time over a long period of time, the autobiographical memory of Israeli veterans of the 1948 War, pertaining to the 1948 Palestinian exodus that led to the creation of the Palestinian refugee problem. Does this memory include the Zionist narrative (i.e. willing flight of the Palestinian refugees) or a critical narrative (i.e. willing flight and expulsion)? One of the primary sources to influence the collective memory of conflicts is the autobiographical memory. This memory is also one of the primary sources for research of the past. Thus, autobiographical memory is of importance.

Methodologically, this is done through an analysis of all 1948 veterans’ memoirs published between 1949 and 2004. Interviews were also conducted with various veterans, to understand the dynamics of their memoir publication.

Empirical findings suggest that during the first period (1949-1968), this memory was exclusively Zionist; during the second (1969-1978), it became slightly critical; and during the third (1979-2004), the critical tendency became more prevalent. Onward, the nine empirical causes for the presentation of exodus the way it was presented are discussed. Theoretical findings relate, inter alia, to the importance of micro factors in shaping the autobiographical memory, assembles seven such theoretical factors, suggests that these factors can influence in two ways (promoting collective memory change or inhibiting it), and that their impact can change over time.

Taken together, the paper contributes empirical and theoretical findings that are based on a solid and wide scope research.

The purpose of this paper is to find the best solver for parallelizing particle methods based on solving Pressure Poisson Equation (PPE) by taking Moving Particle Semi-Implicit (MPS) method as an example because the solution for PPE is usually the most time-consuming part difficult to parallelize.

To find the best solver, the authors compare six Krylov solvers, namely, Conjugate Gradient method (CG), Scaled Conjugate Gradient method (SCG), Bi-Conjugate Gradient Stabilized (BiCGStab) method, Conjugate Gradient Squared (CGS) method with Symmetric Lanczos Algorithm (SLA) method and Incomplete Cholesky Conjugate Gradient method (ICCG) in terms of convergence, time consumption, parallel efficiency and memory consumption for the semi-implicit particle method. The MPS method is parallelized by the hybrid Open Multi-Processing (OpenMP)/Message Passing Interface (MPI) model. The dam-break flow and channel flow simulations are used to evaluate the performance of different solvers.

It is found that CG converges stably, runs fastest in the serial way, uses the least memory and has highest OpenMP parallel efficiency, but its MPI parallel efficiency is lower than SLA because SLA requires less synchronization than CG.

With all these criteria considered and weighed, the recommended parallel solver for the MPS method is CG.