Complex Task Research Articles

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

<span>Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.</span>

Anticipatory postural adjustment deficits in children with developmental coordination disorder during a self-induced prehension task while standing on one leg

Effective postural control is essential for motor skill development, yet the specific nature of anticipatory control in children with Developmental Coordination Disorder (DCD) remains poorly understood for complex or dynamic stability tasks. This study investigated anticipatory postural adjustments (APA) during a self-initiated dynamic stability task. The Can Placement Task (CPT)—a self-initiated dynamic stability task—was performed by 23 children with DCD and 30 typically developing (TD) children aged 9–12 years. The task involved standing on one leg while also repositioning a can on the floor. Center of pressure (COP) movement was recorded by two force platforms during the five phases of the movement. The ground reaction force measured external support during both descent to pick up the can and ascent after replacing the can. The study used a mixed-design approach with group (DCD, TD) as a between-subject factor and condition (can position close or far) and phase of movement as within-subject. Distinct movement control characteristics were shown for children with DCD including a greater range of COP movement and higher COP velocity in the anterior-posterior direction prior to movement initiation compared with TD. The DCD group also relied more on external support during both the downward and upward phases of the CPT and needed more trials to complete the task. Only two significant interaction effects involving Group and the within-subject factors emerged. Children with DCD swayed significantly more at specific phases of the task, especially when coming up and restoring balance, and did not adapt COP velocity as a function of reaching distance. Dynamic control of posture in children with DCD is impaired as they struggle to generate the effective APAs necessary to maintain dynamic stability which leads to greater reliance on external support and more corrective movements. The CPT provides a valuable assessment of posture and dynamic balance control during a complex prehension movement performed on one leg; the task highlights distinct movement patterns between children with and without DCD.

Production sustained release dosage forms from deliquescent substances by solid dispersion systems

Introduction. The production of solid dosage forms from hygroscopic substances is a complex technological task. The addition of hygroscopic substances into a polymer matrix allows to reduce their moisture absorption capacity.Aim. Reducing the hygroscopicity of deliquescent substances and obtaining sustained release solid dosage forms by using solid dispersion systems.Materials and methods. Substance: DEAE; excipients: Kollidon® VA 64, Soluplus®, PEG 1500, PEG 6000, PEG 8000, Poloxamer Kolliphor® P 188 and Kolliphor® P 407; reagents: hydrochloric acid, sodium dihydrogen phosphate, potassium hydrogen phosphate. The melt of the components was obtained on a twin-screw laboratory extruder and cast into silicone molds. The resulting solid dosage forms were studied for disintegration in three environments corresponding to the sections of the human gastrointestinal tract.Results and discussion. The active pharmaceutical substance DEAE is characterized as deliquescent substance. The introduction of DEAE into PEG-polymer matrices made it possible to reduce the hygroscopicity of solid dosage forms in the form of bars. The introduction of solubilizers into the melt made it possible to obtain bars with the appropriate organoleptic properties. The release of DEAE from bear- or toroid-shaped bars occurs on average 29–47 % faster than from heart-shaped bars.Conclusion. Compositions based on Soluplus® and Kolliphor® P 407 have proven promising for further development of tiles containing DEAE. In order to create tiles with a slow release, it is recommended to use heart-shaped forms, as they have a smaller surface area.

Effects of speech tracking method using hearing aids for hearing disorder in older people.

Speech tracking is defined as a complex act or task of listening in which the learner tracks the speech heard and repeats it as accurately as possible while paying attention to incoming contextual information. This ability involves auditory input, speech output, repetition, and divided attention. In this small-scale study, we aimed to use a speech-tracking method to train older adults with hearing loss and examine its effects on hearing loss from multiple perspectives, including hearing handicap, frailty, and neuropsychological testing. Auditory rehabilitation was provided to older patients who purchased hearing aids at the clinic and wanted to engage in rehabilitation. The Hearing Handicap Inventory for the Elderly, Nursing Home Hearing Handicap Index, speech-tracking rate, Token Test, Communication Activities of Daily Living, Kihon Checklist, Mini-Mental State Examination, Digit Symbol Substitution Test, symbol search, and word recall were used for assessments before and after rehabilitation using the speech-tracking method. Changes in scores of each assessment item and sub-item were examined using Wilcoxon's signed-rank test, and changes per question were examined using the sign test. The speech tracking rate (p<0.001), Token Test score (p<0.001), and Mini-Mental State Examination score (p=0.035) improved significantly after the training. Notably, the speech-tracking rate improved for 31 of the 33 participants, with a maximum increase of 19.6 phrases per minute. The combination of a hearing aid and the speech-tracking method improved auditory comprehension.

CoSD: Balancing behavioral consistency and diversity in unsupervised skill discovery

In hierarchical reinforcement learning, unsupervised skill discovery holds promise for overcoming the challenge of sparse rewards commonly encountered in traditional reinforcement learning. However, previous unsupervised skill discovery methods have excelled at maximizing intrinsic rewards but have overly prioritized skill diversity. Unrestrained pursuit of diversity leads skills to concentrate attention on unexplored domains, overlooking the internal consistency of skills themselves, resulting in the state visit distribution of individual skills lacking concentration. To address this problem, the Constrained Skill Discovery (CoSD) algorithm is proposed to balance the diversity and behavioral consistency of skills. CoSD integrates both the forward and backward decomposition forms of mutual information and uses the maximum entropy policy to maximize the information-theoretic objective of skill learning while requiring that each skill maintain low state entropy internally, which enhances the behavioral consistency of the skills while pursuing the diversity of the skills and ensures that the learned skills have a high degree of stability. Experimental results demonstrated that, compared to other skill discovery methods based on mutual information, skills from CoSD exhibit a more concentrated state visit distribution, indicating higher behavioral consistency and stability. In some complex downstream tasks, these skills with higher behavioral consistency exhibit superior performance.

Machine learning approaches for predicting impact sensitivity and detonation performances of energetic materials

Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials. Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge. Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators, potentially accelerating the development process of energetic materials. In this background, impact sensitivity, detonation performances, and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out. Four machine learning algorithms were employed to predict various properties of energetic materials, including impact sensitivity, detonation velocity, detonation pressure, and Gurney energy. Analysis of Pearson coefficients and feature importance showed that the heat of explosion, oxygen balance, decomposition products, and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials. Oxygen balance, decomposition products, and density have a strong correlation with detonation performances. Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark, the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances: oxygen balance values should be between −40% and −30%, density should range from 1.66 to 1.72 g/cm3, HOMO energy levels should be between −6.34 and −6.31 eV, and lipophilicity should be between −1.0 and 0.1, 4.49 and 5.59. These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials, but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.

The ExoMars 2028 WISDOM antenna assembly: Description and characterization

While ground penetrating radars have been extensively researched on Earth, the high-resolution exploration and imaging of the shallow subsurface of celestial bodies in our solar system is still in its early stages, with only a handful of systems capable of the task.Designing high-resolution radar systems can be a complex task due to the large frequency bandwidth required by the antennas to achieve high vertical resolution. The WISDOM GPR, as part of the 2028 ExoMars mission, is a highly capable and challenging instrument in this context, given its fully-polarimetric setup and mission constraints on the operating environment, robustness, as well as mass and size budget.This paper outlines the development and characterization process of the WISDOM antenna assembly, which can serve as a model for future radar systems. Furthermore, it presents the results of the antenna characterization as the foundation for instrument calibration and optimal radar sounding outcomes.

An Exploratory Expert-Study for Multi-Type Haptic Feedback for Automotive Virtual Reality Tasks.

Previous research has shown that integrating haptic feedback can improve immersion and realism in automotive VR applications. However, current haptic feedback approaches primarily focus on a single feedback type. This means users must switch between devices to experience haptic stimuli for different feedback types, such as grabbing, collision, or weight simulation. This restriction limits the ability to simulate haptics realistically for complex tasks such as maintenance. To address this issue, we evaluated existing feedback devices based on our requirements analysis to determine which devices are most suitable for simulating these three feedback types. Since no suitable haptic feedback system can simulate all three feedback types simultaneously, we evaluated which devices can be combined. Based on that, we devised a new multi-type haptic feedback system combining three haptic feedback devices. We evaluated the system with different feedback-type combinations through a qualitative expert study involving twelve automotive VR experts. The results showed that combining weight and collision feedback yielded the best and most realistic experience. The study also highlighted technical limitations in current grabbing devices. Our findings provide insights into the effectiveness of haptic device combinations and practical boundaries for automotive virtual reality tasks.

RoadSitu: Leveraging Road Video Frame Extraction and Three-Stage Transformers for Situation Recognition

Situation recognition is an crucial problem in scene understanding, activity understanding, and action reasoning as it provides a structured representation of the main activity depicted in the image.Semantic role labeling is crucial to situation recognition, which is challenging because a single action can have multiple meanings and purposes depending on its context. Understanding images beyond the highlighted actions requires inferences about the context of the scene, the objects, and their role in the captured event. Recently, situation recognition (SR) has been introduced, which jointly derives a collection of the action (activity), meaning-role, and noun (entities) pairs in the form of moving images. To label these frames as action frames, we must assign nouns (entities) to the role based on the content of the observed image. One of the main challenges is managing the complex dependencies between the assigned roles (nouns) and the predicted action, as the correct role assignment often depends on the accuracy of the action prediction. We introduce, RoadSitu, a road situation recognition that involves generating a structured summary of what is happening in a road scenario using an action and the semantic roles played by agents from a video frame. The action can describe a diverse set of situations, and the same agent can play various roles depending on the situation depicted in the video frame. Therefore, a situation recognition model needs to understand the context of each video frame and the visual-linguistic meaning of the semantic roles of that particular frame. One of the main challenges in this work is the complex task of annotating video frames with semantic roles and handling the structured dependencies between the assigned roles (nouns) and the predicted action (activity). Additionally, the sparsity of meaningful semantic information within road scenarios poses further difficulties. To overcome these challenges, we introduce a novel approach where action recognition and noun estimation work together interactively to form structured summaries of each situation. In experiments using a road video dataset obtained from a South Korean company, RoadSitu achieved significant improvements across various performance metrics, with a Top-1 verb accuracy of 43.46%, Top-5 verb accuracy of 72.48%, and value accuracy of 34.21%, outperforming baseline models such as GSRTR and JSL by 2.4% and 3.86% in Top-1 verb accuracy, respectively. These results demonstrate the effectiveness of our model in handling complex road scenarios.

The Impact of Task Difficulty on Neural Modulation Throughout A Visuomotor Multi-day Practice Training

The effectiveness of rehabilitation is contingent upon the motor recovery process which typically involves long-term motor skill re-acquisition. Given that the learning process can be modulated by task difficulty, elucidating the underlying neural mechanism is essential for optimizing rehabilitation prescription to suit different patient conditions. This study aimed to investigate the impact of task difficulty on cortical response during force-control training via electroencephalography (EEG). An 8-day visuomotor force-tracking training experiment was conducted. Healthy right-handed participants (N=33) were recruited and randomly assigned to 3 groups, and each group was tasked with a different level of difficulty. The task difficulty was manipulated by variation in force-production complexity and execution sequence assignments, with real-time visual feedback provided to participants for self-output adjustment. Behavioral performance was quantitatively assessed using a pre-defined score metric related to performance accuracy. The EEG signals were collected, and corresponding event-related desynchronization (ERD) and relative functional connectivity (FC) during the task execution were analyzed within the alpha- (8-13Hz) and beta- (15-30Hz) bands. A post-training experiment was also performed to evaluate the near-transfer capability of learning. Results showed all the behavioral performances improved during practice, while higher task difficulty level was affiliated with better post-training near-transfer ability. The dynamic neural response to training could be mediated by changes in difficulty level, where increased task complexity corresponded with the heightened activities in the beta-band priorly within the right dorsolateral prefrontal area. Additionally, stronger alpha-band functional connectivity was observed to be predominantly associated with the left motor area (LMA) during challenging tasks, and the intensification in connectivity persisted selectively post-training which appeared to be acritical factor for skill transfer performance improvement. These findings illustrated the dynamic neural mechanism through which task difficulty affects behavioral performance during long-term motor training with accurate force-control purpose. The selectively strengthened functional connectivity may contribute to facilitating new task execution after training interventions. Therefore, beneficial neural modulation can be expected to be feasible by well-designed task difficulty strategies for effective motor rehabilitation.

Is L2 pronunciation affected by increased task complexity in pronunciation-unfocused speaking tasks?

Abstract This study examines the effects of task complexity on second language (L2) pronunciation accuracy and global pronunciation measures in pronunciation-unfocused tasks and assesses the relationship between acoustic and listener-based pronunciation measures. Eighty-two Catalan/Spanish learners of English performed simple and complex versions of a problem-solving monologic speaking task, for which the oral stops /p, t, k/ and vowel contrasts /iː/-/ɪ/ and /æ/-/ᴧ/ were embedded in the lexical items used to perform the task. Pronunciation accuracy was gauged through acoustic measurements of laryngeal timing (voice onset time), vowel contrastiveness and nativelikeness (Mahalanobis distances), and native speakers’ ratings of comprehensibility and accentedness. Results revealed detrimental effects of increased task complexity on the productions of oral stops and speech comprehensibility and accentedness; however, no consistent task complexity effects were found on vowel accuracy. The analysis also revealed an association between segmental accuracy and global dimensions of L2 speech.

Dynamics of Respiratory Rate and Heart Rate Variability when Performing a Cognitive Task of Two Levels of Complexity

The study examined heart rate variability (HRV) and respiratory rate during a cognitive task (reading) at two difficulty levels. Time, frequency and nonlinear analysis of HRV was used. It has been shown that both some indicators of HRV (HR, SDNN, RMSSD, SD2, SD2/SD1) and respiratory rate change with increasing mental load, however, they do not separately demonstrate significant differences at all three stages of the study. Therefore, for the first time for cognitive studies, an integral indicator was used that links the parameters of the heart with respiration – the physiological cost of activity (PhysCost), which was previously used in work with athletes when they performed physical exercises to failure. Changes in the PhysCost showed that differences between a simple and a complex task are observed at all stages of the study. Thus, it has been established that the difference in the functional state of a person in the process of performing a continuous task of two levels of complexity is most reliably revealed when using an integrative indicator connecting the activity of the respiratory system and the circulatory system.

Robot skill acquisition for precision assembly of flexible flat cable with force control

Abstract The flexible flat cable (FFC) assembly task is a prime challenge in electronic manufacturing. Its characteristics of being prone to deformation under external force, tiny assembly tolerance, and fragility impede the application of robotic assembly in this field. To achieve reliable and stable robotic automation assembly of FFC, an efficient assembly skill acquisition strategy is presented by combining a parallel robot skill learning algorithm with adaptive impedance control. The parallel robot skill learning algorithm is proposed to enhance the efficiency of FFC assembly skill acquisition, which reduces the risk of damaging FFC and tackles the uncertain influence resulting from deformation during the assembly process. Moreover, FFC assembly is also a complex contact-rich manipulation task. An adaptive impedance controller is designed to implement force tracking during the assembly process without precise environment information, and the stability is also analyzed based on the Lyapunov function. Experiments of FFC assembly are conducted to illustrate the efficiency of the proposed method. The experimental results demonstrate that the proposed method is robust and efficient.

Simultaneous single image super‐resolution and blind Gaussian denoising via slim ghost full‐frequency residual blocks

AbstractGiven that super‐resolution (SR) aims to recover lost information, and low‐resolution (LR) images in real‐world conditions might be corrupted with multiple degradations, considering basic bicubic down‐sampling as the sole degradation significantly limits the performance of most existing SR models. This paper presents a model for simultaneous super‐resolution and blind additive white Gaussian noise (AWGN) denoising with two components (netdeg and netSR) that is based on a generative adversarial network (GAN) to achieve detailed results. netdeg, featuring residual and innovative cost‐effective ghost residual blocks with a frequency separation module for obtaining long‐range information, blindly restores a clean version of the LR image. netSR leverages slim ghost full‐frequency residual blocks to process low‐frequency (LF) and high‐frequency (HF) information via static large convolutions and pixel‐wise highlighted input‐adaptive dynamic convolutions, respectively. To address the susceptibility of dynamic layers to noise and preserve feature diversity while reducing model’s costs, static and dynamic layer features are combined and highlighted. Diverse and non‐redundant features are then processed using ghost‐style blocks. The proposed model achieves comparable SR results in bicubic down‐sampling scenarios, outperform existing SR methods in the complex task of concurrent SR and AWGN denoising, and demonstrate robustness in handling images corrupted with varying levels of AWGN.

3D vs. 2D-4 K: Performance and self-perception of laparoscopic novices in a randomized prospective teaching intervention using standard tasks and box trainers

ObjectiveThe use of three-dimensional (3D) laparoscopy in surgical practice and training has been an area of research and discussion. Studies have suggested that 3D vision can improve speed and precision compared to traditional two-dimensional (2D) displays, while other authors found no benefits on the learning curves of laparoscopic novices. Modern two-dimensional laparoscopy with a resolution of 3840 × 2160 pixels (2D-4 K) seems to improve laparoscopic view and helps learners orient without stereopsis. However, evidence comparing these systems for laparoscopic training is limited. Therefore, the impact of viewing mode (2D-4 K vs. 3D) on learning and task proficiency remains unclear.DesignWe performed a two-hour teaching intervention on basic laparoscopic skills for novices. In this parallel group randomized study, we randomly assigned learners to 2D-4 K or 3D teaching and performed tasks of increasing difficulty and complexity using standard laparoscopy box trainers. Before the last and most challenging task, learners had to crossover to the other laparoscopy setup. Our hypothesis was that learners would be faster and more precise when using a 3D setup. The primary endpoint was task proficiency measured by speed and failure rate. Secondary outcomes were performance using the viewing mode of the other group without familiarization, self-perception, and career aspirations before and after the teaching intervention, expressed on a Likert scale.SettingThe study was performed by the Department of General, Visceral and Thoracic Surgery at the German Armed Forces Hospital Ulm, which is an academic teaching hospital of the University of Ulm.ParticipantsThirty-eight laparoscopic novices, including medical students and junior residents, participated voluntarily in this teaching intervention. Group allocation was performed via the virtual coin flip method. Apparently, participants and tutors were not blinded to group assignment. No formal approval by the ethics committee was needed for this noninvasive study in compliance with the World Medical Association Declaration of Helsinki as discussed with the ethics committee of the University of Ulm.ResultsThirty-eight laparoscopy novices were randomized in the study. The 3D group (n = 19) was significantly faster than the 2D-4 K group (n = 19) (p = .008) in a standard box trainer model, with 134.45 ± 41.45 s vs. 174.99 ± 54.03 s for task 1 and 195.97 ± 49.78 s vs. 276.56 ± 139.20 s for task 2, and the effect was consistent throughout the learning curve. The failure rate was not significantly affected by the viewing mode. After crossover to the other laparoscopy system, precision and time were not significantly different between the groups. Learners rated the difficulty of laparoscopy lower on a Likert scale after having two hours of basic laparoscopy training. The study was funded by the hospital’s teaching budget.ConclusionsLaparoscopic novices can benefit from a 3D laparoscopy training setup. Exclusive 3D training prior to a complex task on a 2D-4 K setup does not negatively affect the learner’s performance.

Unveiling the Interconnections Between Art, Politics, and Society in the Soviet System Through Art: A Case Study of the Exhibition “1972. Breaking Through the Wall”

Summary This article is dedicated to analysing the exhibition “1972. Breaking Through the Wall” from several perspectives: the curator, the viewers, and the exhibition architect tasked with creating an environment for the art exhibition in an unconventional setting. The article examines the curators’ choices, discusses the work of the exhibition architect within the context of heritage conservation requirements, and presents the results of a viewer survey. This exhibition raised the issue of the significance of alternative creativity, opposed to the official narrative, in a non-free society. It focused on Lithuanian art from the 1960s to the 1980s that countered the official Soviet cultural course. The exhibition’s prehistory includes the historical fact that in the spring of 1972, a young man self-immolated in Kaunas in protest against the lack of freedom. These events had direct and indirect reverberations in the arts, although they were not exhibited at the time due to fear of repression. This difficult experience has left its mark on Lithuanian culture and collective memory. The exhibition highlighted the role of various societal groups in opposing the ruling authorities and establishing personal and creative freedom as the highest value. The art display, composed of works from professionals (fine arts, theatre) and amateurs (rock and big beat musicians) from the 1960s to 1980s who resisted official Soviet art, clearly demonstrated how Kaunas – the capital of Lithuania from 1920 to 1940 – was influenced by the city’s prominent pre-war modernism (environment and lifestyle) and the Soviet ideology imposed on the entire country. The clash of two diametrically opposed ideological stances and artistic principles in Soviet Lithuania led to the emergence of an alternative culture. For the first time in the history of Lithuanian exhibitions, this exhibition showed how the social and the arts were affected by the pressure exerted by the official authorities. The visitor survey showed that the positive reactions to the exhibition were also due to socio-political events that have become embedded in the nation’s memory. The events stored in the collective memory of the nation correlated with specific creative artefacts and historical details, which the younger generation saw for the first time. Noteworthy was the symbiosis of emotional experiences among visitors of different ages, observing exhibits with both artistic and historical value. As indicated by the visitor survey, the exhibition facilitated the establishment of a closer connection between generations. The exhibition’s display in a cultural heritage site – the former post office building (by architect Feliksas Vizbaras, 1932) – was a complex task creatively resolved by contemporary architects.

Framework for Integrating Large Language Models with a Robotic Health Attendant for Adaptive Task Execution in Patient Care

The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI’s ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language.

Serverless computing: How to build and deploy applications without managing infrastructure

Serverless computing is one of the modern computing models where a provider offers a fully managed infrastructure for applications and computing services. In outsourcing complex tasks to servers, programmers are only left with writing code, while the cloud providers deal with challenges such as scaling and server accessibility and maintenance. It also accelerates the development process while lowering operating expenses, enabling scaling with on-demand resources. Other benefits include scaling going hand in hand with the usage, following a usage-based model to charge clients, and requiring little operation overheads. Use cases are as vast as they are diverse, including event-driven microservices, APIs, and data processing pipelines, making serverless an elegant tool for solving state problems in today’s world.

Artificial Intelligence and Artificial Stupidity: The Inseparables

Artificial intelligence (AI) has long been heralded for its ability to simulate human intelligence, enabling machines to perform complex tasks such as decision-making, problem-solving, and data analysis. However, alongside the advancements in AI, the concept of artificial stupidity (AS) has gained attention. AS refers to the limitations and errors made by AI systems, often resulting from incomplete data, biased algorithms, or the inherent restrictions placed on AI to simulate more human-like decision-making. These instances of "stupidity" can lead to nonsensical or harmful outcomes, especially when AI is applied to critical areas such as healthcare, autonomous systems, and legal decision-making. This narrative review explores the duality between AI's potential and its flaws, emphasizing the importance of understanding both AI and AS in developing robust, safe, and ethical AI applications. By addressing the causes of artificial stupidity, such as algorithmic limitations and poor data quality, researchers and developers can improve the reliability and decision-making capabilities of AI systems. There is also the need for human oversight and ethical considerations to mitigate the negative impacts of artificial stupidity, especially in high-stakes environments.

Reconfigurable binary diffractive optical neural network based on chalcogenide phase change material Ge2Sb2Se4Te1

Diffractive optical neural networks (DONNs) possess unique advantages such as light-speed computing, low energy consumption, and parallel processing, which have obtained increasing attention in recent years. However, once conventional DONNs are fabricated, their function remains fixed, which greatly limits the applications of DONNs. Thus, we propose a reconfigurable DONN framework based on a repeatable and non-volatile phase change material Ge2Sb2Se4Te1(GSST). By utilizing phase modulation units made of GSST to form the network's neurons, we can flexibly switch the functions of the DONN. Meanwhile, we apply a binary training algorithm to train the DONN weights to binary values of 0 and π, which is beneficial for simplifying the design and fabrication of DONN while reducing errors during physical implementation. Furthermore, the reconfigurable binary DONN has been trained as a handwritten digit classifier and a fashion product classifier to validate the feasibility of the framework. This work provides an efficient and flexible control mechanism for reconfigurable DONNs, with potential applications in various complex tasks.