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This contribution focuses on the search for wellbeing at a time of the profound crisis of the Anthropocene, which demands that we move away from concepts of personal happiness and begin to imagine a more sustainable, relational wellbeing that includes both human and non-human partners. It argues that in this context female midlife is a fascinating terrain, which highlights the tension between the idea of happiness as a product of individual choice and the indisputable fact of our dependence on others. This article takes two recent German-language novels and explores the contrasting ways in which they figure their protagonist's search for happiness in midlife. Austrian writer Doris Knecht's Eine vollständige Liste aller Dinge, die ich vergessen hatte (2023) centres on a female protagonist who has experienced personal challenges and seeks to find greater happiness in middle age. Although Knecht's novel reveals happiness as a social construct, it ultimately remains entangled in the inward-looking logic of happiness and a good life as the product of individualist optimization. By contrast, German author Judith Hermann's Daheim (2021) situates the protagonist's search for happiness in midlife as part of a broader context of social and environmental crisis and tentatively suggests the potential for a relational wellbeing based in an awareness of the interdependence of humans and the natural world. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Psychodrama und Soziometrie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research investigating young people's social media use has been criticized for its limited theoretical foundations and scope. This paper elaborates young people's social media activity from a socio-ecological evolutionary perspective (SEE), where young people's online exchanges cannot be divorced from the highly competitive and achievement-oriented modern market cultures in which they live. In highly competitive and achievement-oriented forms of life, young people's social media environments are often constituted as dynamic and evolving extrinsically oriented ecological niches that afford for status and identity enhancement while also affording for peer approval, belongingness, and selfworth nested within, and subordinate to, these higher-order affordances. The extrinsic value organization of social media platforms that serve young people's status and identity-enhancement are embodied by a community of mutually interdependent criteria that are evolutionary-based, developmentally salient, and market-driven: physical attractiveness, high (educational and extracurricular) achievements, and material success. Young people's online signaling of these interdependent extrinsic criteria affords for status-allocation and selfenhancement, where each criteria becomes an arena for social competition and identity formation, enabling young people to build personal and optimal models of social success congruent with their own interests and abilities. Young people's status and identity enhancing signaling of these extrinsic criteria is moving toward increasingly idealized or perfect embodiments, informed by accelerating, shortterm positive feedback processes that benefit from the technological affordances and densely rewarding peer environments instantiated on social media. [ABSTRACT FROM AUTHOR]

Surface reconstruction that produces real active species for catalytic reactions generally occurs during electrocatalytic water splitting, but overcoming the reconstruction level‐mass activity‐stability trade‐off is a grand challenge. A cation‐doping in conjunction with a geometrical topology strategy is proposed to concurrently realize deep reconstruction and self‐optimization of FeNi phosphide nanoarrays during an electrochemical activation process. The doped Zn cation induces a deep reconstruction of FeNiP@Fe2P precatalyst by continuously dissolving Fe2P and re‐depositing as amorphous FeOOH that solders Ni2P nanoparticles, forming small ultra‐thin nanosheets with abundant amorphous‐crystalline interfaces for structural stability. Moreover, multichannel topology exhibits an unusual ability to optimize their morphology via finally evolving into multi‐microchannel tubular nanoarrays comprising of interconnected‐nanosheets with a very loose structure for enhanced electrolyte permeability, mass transfer, and accessibility of active sites. The reconstructed Zn‐Ni2P/FeOOH superstructure catalysts reach 10 mA cm−2 current density at an ultra‐low overpotential of 11 mV for hydrogen evolution reactions (HER). Impressively, when assembled as a two‐electrode cell with Zn‐FeNiP@Zn‐Fe2P as anode and Zn‐Ni2P/FeOOH as cathode, it delivers current densities of 10 mA cm−2 at a record low cell voltage of 1.40 V. This strategy provides a novel avenue to promote reconstruction for achieving high catalytic performance. [ABSTRACT FROM AUTHOR]

»Praktische Tagträume: Selbstoptimierung durch Verbraucherlebensstile in China«. The ethos of self-optimization calls on people to imagine a desirable future state of existence and to approach this desired state by adjusting aspects of their everyday lives. This ethos depends on a specific capacity for fantasy. Therefore, this article argues that the concept of self-optimization applies not only to entrepreneurial self-making but also to consumer lifestyles and that it provides a useful critical lens for investigating cultural constructions of labor, leisure, and desire. China has been depicted a place inimical to fantasy, where dreams are denied by social and political pressures. However, in today's China personal dreams symbolize modern subjectivity. Drawing on ethnographic and textual research on self-help psychology in China, this article traces links between self-optimization and various actors, including entrepreneurs, marketers, activists, and authors, who are teaching Chinese youth to craft explicit visions of their ideal life. The article contextualizes projects of self-optimization within China's exploding consumer culture, and in a society where markets are entangled with interpersonal networks and encompassed by state policies. Within these constraints, the logic of self-optimization shapes modest and often commodified pursuits of the good life. [ABSTRACT FROM AUTHOR]

»Selbstoptimierung über Zahlen und digitale Parameter -- Psychische Auswirkungen des digitalen Messens und Vergleichens «. The enormous increase of quantification techniques in the digital age and their expansion into all areas of life generate new mechanisms and imperatives of self-optimization through numbers and digital parameters in relation to body, work, and relationships. The paper discusses features and aporias of increasingly parametric self-optimization as well as the paradoxes of the hermeneutics of numbers that come into play in the process. With reference to case studies from the research project "The Measured Life," psychosocial and psychological meanings of digital measuring, counting, and comparing as well as the fascination for these operations are differentiated. Of central importance are the dynamics of externalizing affect or norm regulations in the context of contemporary measurement-based self-optimization. central concept of optimization of the self in relation to self-perfection and the significance of measurement in the service of parametric self-optimization in digital societies. There is also discussion of the hermeneutics of the figures relevant for self-optimization via measurement. Part 3 is devoted to a description of the study, together with exemplary findings based on brief case studies. Part 4 summarizes and concludes. [ABSTRACT FROM AUTHOR]

»Von der Selbstoptimierung zum Minimalismus - und zurück. Die Versprechen und Praktiken des Fastens in Frankreich«. This article is based on a research project about fasting, an ancient practice that is gaining popularity in France due to its potential benefits for well-being, health, and weight loss. This quest for less seems therefore to meet the neoliberal logic of performance. As such, we consider it to be an original form of self-optimization that would consist in doing more with less. Our paper shows that fasting is not only about maximizing the use of the body but also a means of self-management that can promote greater food reflexivity and even lead to lifestyle changes. The material collected combines an analysis of claims made in favor of fasting on the internet, as well as on smartphone apps, and a qualitative study conducted on people who engage in the practice (intermittent or periodic fasting). Two main findings emerged from this research. Firstly, the trajectories that led the interviewees to fasting show that engagement into this "dietary minimalism" requires to distance oneself from dietary norms and, for some, implies a phenomenon of social distinction. Next, the analysis of both purported benefits and concrete practices reveals the importance of the processual dimension: fasting seems to trigger a deconstruction of the everyday through the experience of less. Finally, food and eating appears to be a particular focal point for self-optimization that then could spill over into other areas. As an initial trial, it can open up other methods of optimization and other forms of simplification: "fasting begets fasting." [ABSTRACT FROM AUTHOR]

»CrossFit und Selbstoptimierung in der Populärliteratur zu Cross- Fit«. CrossFit is one of the fastest growing fitness phenomena of the last decade. Considered the largest fitness business today, CrossFit's widespread popularity has attracted increasing interest from a variety of disciplines. Despite this burgeoning scholarly attention, few analyses have focused on examining CrossFit's discourse of self-optimization and the role it plays in shaping the ideal image of the CrossFitter. The present paper addresses this question by examining the popular literature on CrossFit using a Critical Discourse Approach (CDA). Findings reveal CrossFit's understanding of self-optimization as a polyhedral and demanding process that goes beyond physical discipline to encompass all facets of the self. Five main themes of self-optimization are identified in the popular CrossFit literature: 1) failure and weakness, 2) pain and constant preparedness, 3) integral selfwork and passionate commitment, 4) constant self-improvement, and 5) community support. Drawing on previous research on the topic, the paper also reflects on the relationship between CrossFit and neoliberalism, suggesting CrossFit as a fertile ground for the humanities and social sciences to further explore the intersections between the fields of health, sport, self-optimization, and identity. [ABSTRACT FROM AUTHOR]

For the low parameter tuning efficiency, accuracy and adaptability of the traditional PID parameter tuning method currently adopted in the wind turbine variable-pitch system, this paper presents a method for self-optimization of wind turbine variable-pitch control parameters based on adaptive genetic algorithm, including variable-gain variable-pitch PID parameter self-optimization and tower damping parameter self-optimization. The author first described the basic principle of variable-pitch control system; then based on the genetic algorithm, planned and designed constraint conditions for different wind turbine models, covering initial population selection, self-optimization algorithm constraints and adaptability function design; and finally compared and analyzed the response curve, simulation and field operation results of the traditional parameter tuning method and self-optimization parameter tuning method. The results show that, compared with the traditional parameter tuning method, the self-optimization parameter tuning method reduces the motor speed fluctuation by about 10%, and the fatigue My load of the tower bottom by about 2%. This shows the self-optimization parameter tuning method can improve the tuning efficiency, accuracy and adaptability.

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low‐volume optimization experiments are reported. A Buchwald–Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self‐optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor. [ABSTRACT FROM AUTHOR]

This article argues that the Internet is a relational space. It draws on a provocative, multimedia trilogy by German video artist Brenda Lien to analyse the challenges to notions of sovereign subjectivity posed by the digital age. Lien's Call of ... trilogy explores the ambivalences of life on the participatory Internet, deploying at times shocking images of violence to undermine Web 2.0's appealing aesthetics and cheery injunctions to its users to self-optimize and so gain personal sovereignty. The Internet's spread in the 1990s into mass consumer use coincided with conservative ideologies aimed at promoting the 'sovereign individual' (Dale Davidson and William Rees-Mogg, 2008), capable of profiting in the dotcom era from neoliberal codes of conduct and low regulations. Since then, dataveillance via Internet platforms has grown to reduce technology users' agency and privacy, framing Internet connectivity as a forcibly relational state, despite the individualist self-optimization opportunities it offers. The article reveals networked connectivity as an ambivalent mode of relationality, both necessary due to the communication and affiliation the Internet makes possible, and worrisome due to the connection it forces between technology users and corporations, unanswerably profiting from human needs in a way that challenges the sovereignty of the networked subject radically. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Sexualforschung is the property of Georg Thieme Verlag Stuttgart and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bayesian optimization employs probabilistic surrogate models to effectively address expensive and time‐consuming closed‐loop chemical experimental design. However, traditional Bayesian optimization focuses on reducing the number of iterations and follows an inherently sequential process (with one new data point sampled in each iteration), which is an inefficient means of exploiting and characterizing reactions using parallel microreactors. In this article, we present an approach that overcomes this issue by considering two essential factors: experimental cost sensitivity and parallelization. We propose a novel cost‐aware parallelized Bayesian optimization method (CAPBO) in which the goal is changed from reducing the number of iterations to reducing the experimental cost, with quantitative experimental speed‐up achieved through parallelization. The combination of these two items greatly fits the need of reaction optimization, leading to better optimization effects. Benchmarking and case studies demonstrate that the proposed method significantly outperforms traditional Bayesian optimization and considerably improves the optimization efficiency while maintaining the simplicity of the framework. [ABSTRACT FROM AUTHOR]

Research investigating young people’s social media use has been criticized for its limited theoretical foundations and scope. This paper elaborates young people’s social media activity from a socio-ecological evolutionary perspective (SEE), where young people’s online exchanges cannot be divorced from the highly competitive and achievement-oriented modern market cultures in which they live. In highly competitive and achievement-oriented forms of life, young people’s social media environments are often constituted as dynamic and evolving extrinsically oriented ecological niches that afford for status and identity enhancement while also affording for peer approval, belongingness, and self-worth nested within, and subordinate to, these higher-order affordances. The extrinsic value organization of social media platforms that serve young people’s status and identity-enhancement are embodied by a community of mutually interdependent criteria that are evolutionary-based, developmentally salient, and market-driven: physical attractiveness, high (educational and extracurricular) achievements, and material success. Young people’s online signaling of these interdependent extrinsic criteria affords for status-allocation and self-enhancement, where each criteria becomes an arena for social competition and identity formation, enabling young people to build personal and optimal models of social success congruent with their own interests and abilities. Young people’s status and identity enhancing signaling of these extrinsic criteria is moving toward increasingly idealized or perfect embodiments, informed by accelerating, short-term positive feedback processes that benefit from the technological affordances and densely rewarding peer environments instantiated on social media.

Ensuring reliable and stable communication links between User Equipment (UE) and serving cellular networks during UE movement is one of the significant difficulties facing the deployment of the Fifth Generation (5G) and Sixth Generation (6G) of cellular networks. Therefore, the Handover Parameters Self-Optimization (HPSO) function has been introduced in modern cellular networks to address mobility management issues. Its main purpose to automatically optimize Handover Control Parameters (HCPs) settings. But the probability of estimating suboptimal HCP settings remains an issue, leading to a critical impact on the network performance. This results in an increase in Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The challenge becomes even more critical with the advent of 5G and 6G in cellular networks, owing to various factors. These factors include the extensive deployment of small base stations and the massive growth of connected devices. Despite the development of several HPSO algorithms, the existing techniques fall short of providing optimal solutions. Furthermore, the issue of suboptimal parameters remains unaddressed. This paper introduces an Artificial Intelligence Multiple Linear Regression (AI-MLR) model as an algorithm for optimizing mobility robustness in 5G cellular networks. The objective of the AI-MLR model is to automatically optimize HCP settings based on network experiences, leveraging the Instantaneous Indication Measure (IIM) Function. The AI-MLR model dynamically and instantly estimates HCP settings for UE by utilizing the IIM function. This function evaluates UE experiences through instantaneous Signal-to-Interference-plus-Noise Ratio (SINR) levels from both the target and serving base stations. Initially, the UE captures instantaneous measurements of SINR levels, serving as input parameters for the IIM function. The function then produces an output that acts as an indicator for automating the optimization process of the HCP settings. The proposed algorithm undergoes a comprehensive investigation and validation against various benchmark methods found in the literature, encompassing different mobility speed scenarios over a 5 G cellular network. This study involved the development of a simulation model using Matlab software. Performance evaluation utilized a range of Key Performance Indicators (KPIs), including HOP, HPPP, and RLF. The simulation results demonstrate that the proposed solution achieves significant improvements in terms of HOP, HPPP, and RLF under diverse movement speed scenarios, compared to the algorithms examined in the literature.

Fifth-generation (5G) networks consist of relatively smaller cells compared to legacy networks. Therefore, a user will take a shorter time toward the cell edge. This exposes the mobile station (MS) to frequent handovers, which are bottlenecks affecting the quality of service and user experience. In this study, we introduce a self-optimization method for three pivotal handover control parameters (HCPs): Threshold, Hysteresis and Time-To-Trigger. The proposed approach considers a holistic range of factors to determine the optimal values for these HCPs. These factors include the received power of reference signals (channel conditions), the speed and direction of the user (mobility profile), and the synchronization signal periodicity and handover procedure latency (representing system parameters). Through analytical deliberations, the study establishes a framework for achieving optimal HCPs to minimize the handovers, mitigate the ping-pong effects, reduce handover failures, and sustain a good throughput performance. Furthermore, considering the channel, user, and system parameters allowed cell-specific HCP optimization, enabling the implementation of this method with any of the measurement events outlined in the 3rd Generation Partnership Project (3GPP) release 16 for 5G. This study shows that concurrent self-optimization of Threshold, Hysteresis, and Time-To-Trigger can yield remarkable enhancement of handover performance.

Ensuring reliable and stable communication links between User Equipment (UE) and serving cellular networks during UE movement is one of the significant difficulties facing the deployment of the Fifth Generation (5G) and Sixth Generation (6G) of cellular networks. Therefore, the Handover Parameters Self-Optimization (HPSO) function has been introduced in modern cellular networks to address mobility management issues. Its main purpose to automatically optimize Handover Control Parameters (HCPs) settings. But the probability of estimating suboptimal HCP settings remains an issue, leading to a critical impact on the network performance. This results in an increase in Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The challenge becomes even more critical with the advent of 5G and 6G in cellular networks, owing to various factors. These factors include the extensive deployment of small base stations and the massive growth of connected devices. Despite the development of several HPSO algorithms, the existing techniques fall short of providing optimal solutions. Furthermore, the issue of suboptimal parameters remains unaddressed. This paper introduces an Artificial Intelligence Multiple Linear Regression (AI-MLR) model as an algorithm for optimizing mobility robustness in 5G cellular networks. The objective of the AI-MLR model is to automatically optimize HCP settings based on network experiences, leveraging the Instantaneous Indication Measure (IIM) Function. The AI-MLR model dynamically and instantly estimates HCP settings for UE by utilizing the IIM function. This function evaluates UE experiences through instantaneous Signal-to-Interference-plus-Noise Ratio (SINR) levels from both the target and serving base stations. Initially, the UE captures instantaneous measurements of SINR levels, serving as input parameters for the IIM function. The function then produces an output that acts as an indicator for automating the optimization process of the HCP settings. The proposed algorithm undergoes a comprehensive investigation and validation against various benchmark methods found in the literature, encompassing different mobility speed scenarios over a 5 G cellular network. This study involved the development of a simulation model using Matlab software. Performance evaluation utilized a range of Key Performance Indicators (KPIs), including HOP, HPPP, and RLF. The simulation results demonstrate that the proposed solution achieves significant improvements in terms of HOP, HPPP, and RLF under diverse movement speed scenarios, compared to the algorithms examined in the literature. [ABSTRACT FROM AUTHOR]

Dimensional accuracy is a highly important quality criterion for injection molded parts and due to the decreasing tolerance criteria an ongoing issue for mold makers, part designers and manufacturers. Variations in the molding process regarding temperature, pressure and thus the specific volume are leading to inner stress distributions within the part, which ultimately cause warpage. To reduce warpage, a homogenization of the local melt properties, such as the temperature, cooling rate or specific volume is necessary. To be able to manipulate these melt properties locally, a mold with a highly segmented temperature control, consisting of 18 heating ceramics and carbon dioxide expansion chambers was designed. This work seeks to reduce warpage using a newly developed control loop that adjusts target values for the upcoming molding cycle while considering the control error of the previous cycle. The self‐optimizing control loop‐ aiming to homogenize the local mold temperatures over multiple cycles‐ was evaluated in experimental trials. Test specimens were produced, and measured for warpage. Compared to the reference (no control), the warpage of a plate‐shaped geometry was reduced by 10 % with the mold temperature homogenization approach. [ABSTRACT FROM AUTHOR]

Effective mobility management in heterogeneous networks is significant in ensuring seamless handovers (HOs) between diverse cell types, especially as users move between macrocells, small cells, and femtocells. The widespread and overlapping deployment of diverse cells raises the HO probability occurrence massively, particularly when users are on the move and connected to multiple cells simultaneously to ensure uninterrupted connectivity. This phenomenon leads to an increase in HO ping pong (HOPP) and HO failure (HOF) occurrences, ultimately degrading network performance. In this context, this paper proposes a self-optimization algorithm to address the contradiction in the optimization tasks of mobility robustness optimization (MRO) and load balancing optimization (LBO) functions. The algorithm aims to facilitate seamless user communication as individuals move across various deployment scenarios. The MRO function leverages two key parameters: the reference signal received power (RSRP) levels of serving and target cells, as well as user movement speed. On the other hand, the LBO function takes into account the traffic load of serving and target cells to determine the suitable values of HO control parameters. Moreover, our research contributes to the present network optimization challenges and positions itself as an enabler for the seamless integration of emerging technologies. As the wireless ecosystem continues to evolve, with the advent of edge computing and network slicing technologies, our self-optimization algorithm offers adaptability and scalability to meet the evolving demands of next-generation wireless networks. This forward-looking solution benefits network operators and end-users by providing robust and efficient mobility management. The proposed algorithm demonstrates its effectiveness through comprehensive simulations by significantly reducing HOPP and HOF compared to investigated methods selected from the literature, showcasing its potential to enhance network performance and user experience.

Abstract Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low‐volume optimization experiments are reported. A Buchwald–Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self‐optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using

Mobile connections and applications are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide, which greatly increases the demands on data traffic. This led to considering millimeter-wave bands and ultra-dense deployment as part of the key enabler solutions in fifth-generation (5G) networks. However, these solutions radically increase the number of handovers (HOs), thus increasing the rate of unnecessary HO and dropping call probabilities. In this regard, optimizing HO control parameters appropriately is the main factor that can efficiently address HO issues during user mobility. This paper proposes a fuzzy-coordinated self-optimizing HO scheme to achieve a seamless HO while users move in multi-radio access networks. The proposed scheme resolves the conflict between mobility robustness and load balancing functions by utilizing a fuzzy system considering three input parameters: signal-to-interference-plus-noise ratio, cell load and UE speed. Simulation results show that the proposed scheme manages to control the mobility optimization in terms of ping-pong HO, radio link failure and HO latency over different mobile speed scenarios. Moreover, the proposed scheme reduces the outage probability compared to other schemes from literature.

This article examines the introduction of "sensitivity training" to 1970s Swedish work life. Drawing upon a range of empirical materials, I explore the politics that were involved in the process of translating and adapting this group dynamic method to the Swedish context and consider how its proponents argued for its value. By approaching sensitivity training as an attempt to govern, shape, and regulate both human beings and the work organizations of which they were a part, I argue that sensitivity training presents an unexpectedly early example of a governing rationality that has elsewhere been described and theorized as "neoliberal." The fact that sensitivity training was established in Swedish work life already in the early 1970s thus challenges the historiography of neoliberal modes of government, which have elsewhere been associated with a neoliberal shift in state policies occurring in the 1980s and 1990s. The article demonstrates how emotionally liberating practices in the late 1960s and early 1970s were embraced by some of the most politically influential actors in contemporary Swedish society, such as the corporate sector and the trade unions. As blue‐collar trade unions and social democrats voiced increasingly far‐reaching demands concerning workplace democracy and improved workplace conditions, advocates of sensitivity training presented their method as crucial to the process of "democratizing" and "humanizing" Swedish work life. Intimately associated with the new therapies of humanistic psychology, sensitivity training was used within the corporate sector to foster a more emotional and authentic leadership style that would embrace the values of emotional awareness, self‐expression, and self‐actualization. The crying boss emerged in this context as a key figure in the project of creating a "democratic" and psychologically satisfying organization. Yet, sensitivity training was also described as a means for companies to make better use of what was now asserted as their most important economic asset: the human being. From the outset, the idealistic vision of an emotionally liberated, democratic workplace was thus entangled with a specific kind of economic rationality, in which the emotionally liberated, self‐actualizing individual emerged as a capital or asset that would be better utilized if the organization allowed—even encouraged—employees to engage in their own well‐being and self‐optimization. [ABSTRACT FROM AUTHOR]

Energy consumption in cloud computing plays a vital role in operating costs for both the service provider and the cloud user. The cloud is scalable and can provide access as per demand. Due to this, resource access requests are increasing and submitted to the server. To manage all requests, scheduling is the solution to assign requests with the quality of service. To avoid high operating costs, resource scheduling needs to be energy-aware. In this paper, energy-aware resource scheduling in the cloud is proposed. The total resource utilization of each resource has been calculated, and energy is optimized through the antlion optimization algorithm to avoid high power consumption. The resource is identified with its best utilization value and assigned to submitted workloads on a priority basis. The experimental results of the proposed work are analyzed with existing autonomic frameworks, and it is found that the proposed work performs exceptionally well. In addition to addressing the increasing resource access requests, the proposed energy-aware resource scheduling in cloud computing also focuses on optimizing overall system performance. The antlion optimization algorithm is employed to efficiently manage energy consumption, ensuring that power usage is minimized without compromising the quality of service. The algorithm calculates the total resource utilization for each component in the cloud infrastructure, allowing for a thorough understanding of system dynamics. The paper optimizes cloud computing by prioritizing efficient and capacity-based resource assignment, improving responsiveness, user experience, and reducing costs. The approach also promotes environmental sustainability. Experimental results validate its effectiveness in energy efficiency and system responsiveness, contributing to advanced resource management in cloud computing for sustainable and cost-effective services. [ABSTRACT FROM AUTHOR]

The recognition of different welding defects is important for the assessment of the safety of welded structures. This study proposes a method to recognize defects in stainless steel welds based on multi-domain feature expression and self-optimization. This is because of the poor detection of feature-related information from signals, inadequate feature extraction by the convolutional network, the limited capability of intelligent techniques, network redundancy, and a lack of self-adaptive capability in prevalent methods, A 1D ultrasonic detection dataset of austenitic stainless steel welds in the time domain (TD) is first constructed and the 1D TD signals are rendered in the time–frequency domain, Gramain angular field, and the Markov transition field. The aim is to enrich the feature expression of 1D ultrasonic echo data of the weld defects. A comparison among a variety of lightweight convolutional neural networks on multi-spatial domain datasets is used to identify a combination of network and spatial domain datasets that are suitable for recognizing welding defects. Finally, a multi-scale depthwise separable convolution is designed, and is subjected to adaptive compression and parameter-adaptive optimization based on the sparrow search algorithm to construct the self-optimizing lightweight multi-scale MobileNetV3 (SLM-MobileNetV3) model. The results of experiments showed that the SLM-MobileNetV3 model has an accuracy of 97.26% for the recognition of five types of defects: inclusion, crack, porosity, incomplete penetration, and a lack of fusion. The time required for testing a single image was 2 ms. Experimental analysis showed that the proposed method can improve the accuracy of defect recognition while using few parameters and computational cost, and is generalizable. [ABSTRACT FROM AUTHOR]

With the rapid growth of data traffic and mobile devices, it is imperative to provide reliable and stable services during mobility. Heterogeneous Networks (HetNets) and dense networks have been identified as potential solutions to address the upcoming capacity crunch, but they also pose significant challenges related to handover optimization. This paper presents a comprehensive review of recent handover decision algorithms in HetNets, categorizing them based on their decision techniques and summarizing their input parameters, techniques, and performance evaluations. Our study highlights the technical challenges and opportunities related to handovers in HetNets and dense cellular networks and provides key findings from recent studies. The significance of this survey is to provide a comprehensive overview of handover decision algorithms in HetNets and dense cellular networks, which can aid in the development of more advanced handover optimization approaches. [ABSTRACT FROM AUTHOR]

The contemporary culture is explicitly permeated with ideas of Transhumanism. Based on principles such as technologizing and self-optimization, Transhumanism promotes the idea of constantly pushing the boundaries of immediate physical and psychological human existence. Self-optimization is about improving physical and cognitive as well as emotional and social performance. In this article, we want to explore the question of how religious education reflection takes up the transhumanist concept of self-optimization and to what extent it can be addressed in religious education. We will first focus on the basics of Transhumanism and then we will examine the topic of human nature and corporeality from a transhumanist perspective and try to implement it didactically in religious education.

Resident load is one of the important components of seasonal peak load and has huge demand response (DR) potential. But its randomness and decentralization limit the ability to flexibly participate in DR interactions. In view of the characteristics of resident load and the uncertainty of response behavior, the resident load model is established with the characteristics of load curve, historical DR participation and response degree as parameters. And the distribution parameters are identified through actual resident data sets. Furthermore, a method of forming a priority queue based on historical response effects is proposed. On this basis, the resident DR cost model is established, and the optimal resident DR strategy is obtained with the goal of cost minimization, so that the DR cost can be reasonably controlled under the premise of accurately achieving the load reduction target. The priority queue is updated and corrected successively in multiple DR events to realize automatic optimization of response strategies. Finally, the calculation example verifies the effectiveness of the proposed resident load model and the resident DR strategy self-optimization method.

The intimidating surge in the procurement of Distributed Energy Resources (DER) has increased the number of prosumers, creating a new possibility of local energy trading across the community. This project aims to formulate the peer-to-peer energy (P2P) sharing model to encourage the DERs to share surplus energy among the consumers. An effective pricing method is developed based on the supply-demand ratio (SDR) with the importance of self-optimization, which allows the prosumers to maximize their energy sharing and profits. To implement this pricing method, a simplified dynamic matchmaking algorithm has been deployed to introduce the Outstanding Prosumer to interact with existing consumers to increase the efficiency and profitability of the trade network. Consumers also benefit from this model, as they can pick the most economical energy supplier instead of relying on the utility grid. The prosumer with high excess energy and the consumer with the highest energy demand will be prioritized to maintain the SDR ratio to one or greater than one. Here, all the above-stated features of the peer-to-peer energy trading have been demonstrated with some calculations to back up some tangible results. Finally, a case study is simulated among the residents of Dhaka, Bangladesh, to demonstrate how peers can profit from participating in trading at a given time. Comparing the results with and without P2P trading, there has been a 17.54% reduction in an electric bill on a typical day of July, and a 49.53% reduction in the interaction with the grid. [ABSTRACT FROM AUTHOR]

The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data's temporal information. In addition, this study used Bayesian optimization to solve the problem of the model's inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration. [ABSTRACT FROM AUTHOR]

The in situ surface reconstitution of a catalyst for hydrogen evolution refers to its structure evolution induced by strong interactions with reaction intermediates during the hydrogen evolution reaction (HER), which eventually leads to the self-optimization of active sites. In consideration of the superior performance that can be achieved by in situ surface reconstitution, more and more attention has been paid to the relationship between active site structure evolution and the self-optimization of HER activity. More and more in situ and/or operando techniques have been explored to track the dynamic structural evolution of HER catalysts in order to clarify the underlying mechanism. This review summarizes recent advances in various types of reconstruction such as the reconfiguration of crystallinity, morphological evolution, chemical composition evolution, phase transition refactoring, surface defects, and interface refactoring in the HER process. Finally, different perspectives and outlooks are offered to guide future investigations. This review is expected to provide some new clues for a deeper understanding of in situ surface reconfiguration in hydrogen evolution reactions and the targeted design of catalysts with desirable structures. [ABSTRACT FROM AUTHOR]

Copyright of Seminare Learned Investigations / Seminare. Poszukiwania naukowe is the property of Francis de Sales Scientific Society / Towarzystwo Naukowe Franciszka Salezego and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ultra-dense heterogeneous networks (HetNets) are deployment scenarios in the advent of fifth generation (5G) and beyond network generations. A massive number of small base stations (SBSs) and connected devices have been exponentially increasing. This has subsequently led to a rise of several mobility management issues which require optimization techniques to avoid performance degradation. Machine learning (ML) is a promising approach for future mobile communication networks (5G and beyond). It has the ability of improving the efficiency of complicated heterogeneous and decentralized networks. ML has proven to be significant in the mobility management field since it optimizes handover control parameters (HCPs) over various dynamic environments. To the best of the authors’ knowledge, no comprehensive survey deeply discussing a state-of-the-art ML algorithms in mobility robustness optimization (MRO) functions. However, each summarized algorithm in this study includes deployment scenario, ML type, methodology used, criteria, HCPs, key performance indicators (KPIs), simulators, and achievements which can assist researchers for future investigations in MRO functions. In addition, this study serves as a guide in the selection of proper optimization algorithms according to the outcomes of each algorithm. Furthermore, this study presented the common types of ML and the techniques used from each type to optimize the HCPs of the MRO functions. Moreover, high-mobility-aware and network topologies are presented in MRO function for further system enhancements. Besides, the survey further highlights several potential problems for upcoming research and provides future directions to address the issues of next generation wireless networks.

Urban rivers are complex ecosystems that directly determine the living environment of human beings. Monitoring the urban river water quality indexes is a challenge in water quality evaluation. The purpose of this study was to propose a multi-source remote sensing water quality inversion method based on a small number of samples to solve the problem of scale inconsistency among multi-source remote sensing data, so as to achieve large-scale and efficient inversion of urban river water quality. Since there is a very important problem that the complex nonlinear relationships must be solved between simple ground point data and remote sensing data in water quality inversion, a novel self-optimizing machine learning monitoring method is proposed, which can automatically find the optimal parameters of the model from a small number of samples, and reduce the training time. Meanwhile, in order to strengthen the correlation between water quality parameters and remote sensing data, the feature enhancement method was used for generating the input data. Moreover, to solve the problem of the multi-source data quantity and quality, the spatial mapping method was used to achieve consistency in the water quality information since these data have different nonlinear characteristics. The experimental results show that for unmanned aerial vehicle (UAV) images, the R2 of chlorophyll a (Chla), turbidity (TUB), and ammonia nitrogen (NH3-N) can reached 0.917, 0.877 and 0.846, respectively. Using a satellite image, the R2 of Chla, TUB, and NH3-N can reach 0.827, 0.679 and 0.779, respectively. This method provides a new way to realize the integration of air-space-ground monitoring of urban inland rivers in the future.

Driven by growing mobile traffic, millimeter wave (mmWave) communications have recently been developed to enhance wireless network capacity. Due to insufficient coverage and the lack of support for mobility, mmWave is often deployed in the ultra-dense small cells of the 5G heterogeneous network. In this article, we first summarize the characteristics of the 5G heterogeneous network from the viewpoints of devices, spectra, and networks. We then propose a triple-band network structure which incorporates licensed bands, sub-6GHz unlicensed bands, and mmWave bands to support various types of mobile users. Based on the novel network structure, we further propose a self-optimizing traffic steering strategy which can intelligently steer traffic to specific networks and spectra according to the dynamic network and traffic environments. Several use cases are also discussed to facilitate the implementation of our proposals. Finally, we present numerical results to demonstrate that the proposed network structure and strategy can effectively enhance the system throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key factors, such as the use of Millimeter Wave (mmWave) and Terahertz, a higher number of deployed small cells, massive growth of connected devices, the requirements of a higher data rate, and the necessities for ultra-low latency with high reliability. Therefore, providing robust mobility techniques that enable seamless connections through the UE's mobility has become critical and challenging. One of the crucial handover (HO) techniques is known as mobility robustness optimization (MRO), which mainly aims to adjust HO control parameters (HCPs) (time-to-trigger (TTT) and handover margin (HOM)). Although this function has been introduced in 4G and developed further in 5G, it must be more efficient with future mobile networks due to several key challenges, as previously illustrated. This paper proposes a Robust Handover Optimization Technique with a Fuzzy Logic Controller (RHOT-FLC). The proposed technique aims to automatically configure HCPs by exploiting the information on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and UE velocity as input parameters for the proposed technique. The technique is validated through various mobility scenarios in B5G networks. Additionally, it is evaluated using a number of major HO performance metrics, such as HO probability (HOP), HO failure (HOF), HO ping-pong (HOPP), HO latency (HOL), and HO interruption time (HIT). The obtained results have also been compared with other competitive algorithms from the literature. The results show that RHOT-FLC has achieved considerably better performance than other techniques. Furthermore, the RHOT-FLC technique obtains up to 95% HOP reduction, 95.8% in HOF, 97% in HOPP, 94.7% in HOL, and 95% in HIT compared to the competitive algorithms. Overall, RHOT-FLC obtained a substantial improvement of up to 95.5% using the considered HO performance metrics. [ABSTRACT FROM AUTHOR]

The implementation of self‐optimizing flow reactors has been mostly limited to model reactions or known synthesis routes. In this work, a self‐optimizing flow photochemistry platform is used to develop an original synthesis of the bioactive fragment of Salbutamol and derivatives. The key photochemical steps for the construction of the aryl vicinyl amino alcohol moiety consist of a C−C bond forming reaction followed by an unprecedented, high yielding (>80 %), benzylic oxidative cyclization. [ABSTRACT FROM AUTHOR]

In response to the current need for intelligent optimization of dredging production efficiency in the cutter suction dredger dredging control system, an intelligent self-optimization method for the construction parameters of a cutter suction dredger based on reinforcement learning was proposed. Firstly, the control variables in the construction process were selected by using the method of information gain rate to form a multiple training data set, and then a reinforcement learning environment model that includes continuous action space, state transition, and reward and punishment functions was established. The agent executes instructions and feedback state information according to the random actions given by the algorithm, gradually obtain the optimal strategy through interactive learning with the environment, and realize the self-optimization of dredging parameters of the cutter suction dredger. Simulation experiments were carried out using data collected from real ships, and the results show that the self-optimization method of dredging parameters based on reinforcement learning can quickly and effectively learn and achieve goals under uncertain environmental conditions, which proves the rationality and effectiveness of this method. [ABSTRACT FROM AUTHOR]

In order to suppress the secondary ripple of DC microgrid bus voltage, a centralized compensation self-optimization strategy of DC active power filter is proposed in this study. Based on the voltage/current double closed-loop control of bi-directional DC/DC converter, the DC bus voltage ripple control is added. A band-pass filter is introduced to eliminate the phase lag problem when the low-pass filter is used to extract the ripple in the traditional control method. The impedance coefficient K which is an important control parameter, is obtained by iterative self-optimization method to realize the real-time tracking and centralized compensation of DC bus voltage ripple by DC active power filter. In MATLAB/Simulink, simulation model of DC microgrid including an interlinking converter, two distributed generations, a DC load, a single-phase AC load and a DC-APF is built, and the corresponding experimental platform is established. Simulation and experimental results validate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]