Future Users Research Articles

HOW TO REDUCE TRANSMISSION OF RESPIRATORY INFECTIOUS DISEASE IN INTERIOR SPACES: AN EMPIRICAL STUDY WITH INTERIOR DESIGN PROFESSIONALS

ABSTRACT This study was motivated by the need for empirical evidence to inform design guidelines for interior workspaces during the COVID-19 pandemic. This research had two goals: to identify the features of interior spaces and behaviors in Korea where many confirmed cases of COVID-19 were found; and to examine design professionals’ opinions on workplace management and operation to reduce transmission of respiratory infectious disease. A content analysis of newspaper articles on confirmed cases was conducted and a survey was administered to design professionals. The analysis identified common features of the interior spaces with confirmed cases of COVID-19. Based on the analysis of the article contents, a questionnaire was constructed. The questionnaire asked design professionals to suggest interior design considerations that could prevent the spread of the virus in interior spaces. They emphasized the separation of individual spaces from shared spaces; installing partitions in rooms that could not be divided; ending two-way traffic in narrow hallways; reducing room density and avoiding crowding; adopting touchless technologies and no-touch access to interior spaces; automating interior equipment and facilities; and encouraging office users to work from home if possible. These findings should be factored into interior space planning and management to reduce the risk of exposing future users of these spaces to infectious respiratory diseases.

Assessment of Ghana's current energy types to meet tomorrow's needs

Ghana is currently facing challenges in aligning its energy options with future energy demands and reducing its dependency on fossil fuels. This study assessed Ghana's current energy types and their potential to meet future energy needs. A structured questionnaire with a cross-sectional survey and random sampling technique was employed to gather information on energy choices, drivers and challenges from 868 respondents. A multiple linear regression model was used to evaluate the impact of energy drivers on preferences in Ghana. Results showed that 82 % of Ghanaians are ready to transition to cleaner energy sources, with preferences for hydropower/grid electricity (45.70 %) and natural gas/LPG (32.90 %) and biofuels (12.00 %). Economic (16.20%) and population (15.50%) growth are the main drivers of energy transitions, while challenges include high initial costs (11.20%) and limited awareness (4.90%). Strategies such as financial support, education, renewable energy promotion, technological advancement and international collaboration should be promoted to actualise Ghana's transition to future renewable energy usage.

111 Current and future usage of soybean meal in swine diets in China

Abstract The objective of this presentation was to introduce the utilization tendency and research progress of soybean meal (SBM) in swine diets in China. As for the utilization tendency of SBM in China, it not only brings great pressure to the food security of China, but also directly pushes the high production cost of animal products. In 2022, 117 million tons of protein feed was consumed by the animal husbandry in China, accounting for nearly 1/3 of the total protein feed consumption in the world. Thus, the application of low-protein diet has been promoted with the strong support of the Ministry of Agriculture and Rural Affairs in China since 2018. With the related techniques, including popularizing low protein diet, developing new protein feed resources, promoting precise nutrition, and feeding technology, even though the compound feed output has increased by more than 84 million tons in 2022 in China, the SBM consumption in feed has reduced 6%. Recently, the geopolitical challenges, regional unrest and climate change tendency have increased the great uncertainty of protein feed supply, which requires us to deeply understand the significance of SBM reduction and substitution in animal feed. As for the research progress, the available energy contents of SBM were determined on growing pigs. Barrows [n = 24; initial body weight (BW) = 36 ± 1.4 kg] were randomly allotted to 4 diets containing SBM with either corn basal or corn-soybean meal basal, and total feces and urine collection method and indirect calorimetry method were used to determine the available energy of SBM. The results showed that the average digestible energy (DE), metabolizable energy (ME), net energy (NE), and retained energy (RE) values in SBM [17.36, 16.52, 10.62, and 5.06 MJ/kg dry matter (DM), respectively] determined using the corn-SBM-basal diet were not different from the those determined using the corn-basal diet. Moreover, the best fitted prediction equations developed based on 22 SBM samples produced from soybeans from China (n = 6), USA (n = 6), Brazil (n = 7) or Argentina (n = 3) were: DE = 38.44 - 0.43 CF - 0.98 GE + 0.11 ADF (R2 = 0.67, P < 0.01) and ME = 2.74 + 0.97 DE - 0.06 CP (R2 = 0.79, P < 0.01). The in vitro digestion kinetics of SBM from different sources showed that the release of amino acids in the 0-10 min time interval was significantly negatively correlated with the NE of SBM (R2 = -0.34).

The SARAO MeerKAT 1.3 GHz Galactic Plane Survey

ABSTRACT We present the SARAO MeerKAT Galactic Plane Survey (SMGPS), a 1.3 GHz continuum survey of almost half of the Galactic Plane (251° ≤l ≤ 358° and 2° ≤l ≤ 61° at |b| ≤ $1{_{.}^{\circ}}5$). SMGPS is the largest, most sensitive, and highest angular resolution 1 GHz survey of the plane yet carried out, with an angular resolution of 8 arcsec and a broad-band root-mean-square sensitivity of ∼10–20 μJy beam−1. Here, we describe the first publicly available data release from SMGPS which comprises data cubes of frequency-resolved images over 908–1656 MHz, power-law fits to the images, and broad-band zeroth moment integrated intensity images. A thorough assessment of the data quality and guidance for future usage of the data products are given. Finally, we discuss the tremendous potential of SMGPS by showcasing highlights of the Galactic and extragalactic science that it permits. These highlights include the discovery of a new population of non-thermal radio filaments; identification of new candidate supernova remnants, pulsar wind nebulae and planetary nebulae; improved radio/mid-infrared classification of rare luminous blue variables and discovery of associated extended radio nebulae; new radio stars identified by Bayesian cross-matching techniques; the realization that many of the largest radio-quiet Wide-field Infrared Survey Explorer (WISE) H ii region candidates are not true H ii regions; and a large sample of previously undiscovered background H i galaxies in the Zone of Avoidance.

Optimizing Fuel Injection Timing for Multiple Injection Using Reinforcement Learning and Functional Mock-up Unit for a Small-bore Diesel Engine

<div>Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [<span>1</span>]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NO<sub>x</sub>–soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO–soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot–main and a pilot–main–post-fuel injection strategy. Change of fuel injection quantity was not attempted in this study as the main objective was to demonstrate the use of RL algorithms while maintaining a constant indicated mean effective pressure. A change in fuel quantity has a larger influence on the indicated mean effective pressure than a change in fuel injection timing. The focus of this work was to present a novel methodology of using the 3D combustion data from analysis software in the form of a functional mock-up unit (FMU) and showcasing the implementation of a RL algorithm in Python language to interact with the FMU to reduce the NO and soot emissions by suggesting changes to the main injection timing in a pilot–main and pilot–main–post-injection strategy. RL algorithms identified the operating injection strategy, i.e., main injection timing for a pilot–main and pilot–main–post-injection strategy, reducing NO emissions from 38% to 56% and soot emissions from 10% to 90% for a range of fuel injection strategies.</div>

Simulation Games as an Alternative Formative Assessment Tool for Experiential Learning in VET Marketing Education: A Desk-Research Review

Simulation games continue to be widely used in marketing education. Based upon a review of simulation tools from the past decade, this study gathers insights on how simulations can be used as an effective assessment tool to enhance learning in marketing and digital marketing vocational subjects. In particular, this research looks more closely at the opportunities afforded by simulation games for experiential learning in relation to formative assessments as well as the challenges presented by the application and validity of the games. This study made use of the desk-based approach as a line of research study while making use of secondary data that is available in competent scholarly literature related to simulation games as an alternative formative assessment tool for experiential learning in VET marketing education. The methodological flow followed in this research used the five phases implementation of industry 4.0 technologies as suggested by Garrido-Lova (2021). This research is original as, through desk-research, the study gave an overview of how simulation games can contribute towards experiential learning. The study indicated that a plethora of evidence points towards the number of pedagogical effects students benefit from when assessments take a formative approach and are carried out through a simulation game. This research should be of keen interest to current and future users of marketing simulation games who might have questions about how to use them effectively in a vocational setting and for assessment purposes.

A Comprehensive Review of Modern Methods for Load Prediction in the Smart Grid

: Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and Accuracy. Machine learning-based techniques showed a reduction of 9.17% in MAPE, 0.0429% in RMSE, and 5.23% in MSE, and achieved 90% accuracy. Deep learning-based techniques resulted in a 9.61% decrease in MAPE and achieved 91% accuracy. Bioinspired techniques provided a reduction of 9.66% in MAPE, 0.026% in RMSE, and 5.24% in MSE, and achieved 95% accuracy. These findings concluded that optimization techniques are more encouraging in predicting load demand and, as a result, can represent a reliable decision-making tool.

Postoperative Opioid Usage and Disposal Strategies After Arthroscopic Procedures in a Young Cohort: A Prospective Observational Study.

Although several studies have noted that patients are routinely overprescribed opioids, few have reported usage after arthroscopic surgery. To determine opioid consumption and allocation for unused opioids after common arthroscopic surgeries. Case series; Level of evidence, 4. Patients between the ages of 15 and 40 years who were scheduled to undergo anterior cruciate ligament reconstruction (ACLR), labral repair of the hip or shoulder, meniscectomy, or meniscal repair were prospectively enrolled. Patients were prescribed either 5 mg hydrocodone-325 mg acetaminophen or 5 mg oxycodone-325 mg acetaminophen based on surgeon preference. Patients completed a daily opioid usage survey during the 2-week postoperative period. In addition, patients completed a survey on postoperative day 21 inquiring about continued opioid use and medication disposal, if applicable. Opioid medication consumption was converted to morphine milligram equivalents (MMEs). Of the 200 patients who were enrolled in the study, 176 patients had sufficient follow-up after undergoing 85 (48%) ACLR, 26 (14.8%) hip labral repair, 34 (19.3%) shoulder labral repair, 18 (10.2%) meniscectomy, and 13 (7.4%) meniscal repair procedures. Mean age was 26.1 years (SD, 7.38); surgeons prescribed a mean of 26.6 pills whereas patients reported consuming a mean of 15.5 pills. The mean MME consumption in the 14 days after each procedure was calculated: ACLR (95.7; 44% of prescription), hip labral repair (84.8; 37%), shoulder labral repair (57.2; 35%), meniscectomy (18.4; 27%), and meniscal repair (32.1; 42%). This corresponded to approximately 39% of the total opioid prescription being utilized across all procedures. Mean MME consumption was greatest on postoperative day 1 in hip, shoulder, and meniscal procedures and on postoperative day 2 in ACLR. Only 7.04% of patients reported continued opioid use in the third postoperative week. Patients had a mean of 11 unused pills or 77.7 MMEs remaining. Of the patients with remaining medication, 24.7% intended to keep their medication for future use. The results of our study indicate that patients who undergo the aforementioned arthroscopic procedures consume <75 MMEs in the 2-week postoperative period, translating into a mean of 10 to 15 pills consumed. Approximately 60% of total opioids prescribed went unused, and one-fourth of patients intended to keep their remaining medication for future usage. We have provided general prescribing guidelines and recommend that surgeons carefully consider customizing their opioid prescriptions on the basis of procedure site to balance optimal postoperative analgesia with avoidance of dissemination of excess opioids.

Hydrogen permeability testing of fibre reinforced thermoplastics under cryogenic conditions – validation of a test rig concept

The need to decrease the gross weight of cryogenic hydrogen fuel systems in future zero emission mobility leads to increasing activities in the field of cryogenic lightweight engineering. Fibre reinforced thermoplastic composite materials (FRT) are considered for cryogenic applications despite their bias towards permeation. However, permeation through plastic materials is of major concern in cryogenic applications. Even tiny fluxes can very much compromise the insulating power of high vacuum spaces required for insulation of cryogenic systems such as tank structures or transfer lines. Hence, it is essential to qualify those FRT in terms of their hydrogen permeability for future usage in mobile cryogenic applications. The conventional concepts for measuring permeability in plastic materials are not sufficient for cryogenic measurements of FRT as shown in a previous paper. For that, a novel laboratory test rig concept was proposed. In this paper, we validate this concept and show its eligibility for measuring permeation of hydrogen through thermoplastic materials. Therefore, we report results of helium permeation through polytetrafluoroethylene (PTFE) at room temperature and compare those results to literature permeability data.

Gamification as an innovative method in user experience design

AbstractThe growing research field of gamification promises new insights and innovative methods for the active design of user experience. This article examines the extent to which gamification complements the established methods of user experience design in the context of product development. To this end, assessment criteria are proposed that can be used to evaluate human-centred design methods. A qualitative comparison is then used to determine the added value of the innovative field of gamification for future user experience design in consumer products.

Exploring a web-based interactive writing assessment WISSE: User experiences

This paper aims to investigate the user experiences of Web-based Integrated Writing Assessment (WISSE) developed to help lecturers and learners provide and navigate feedback for academic writing in English. User experience is one of the crucial elements for product success and reception in Research and Development (R &amp; D). Aspects of user experience highlighted in this study are web features, ease of use, and design. The participants involved were two lecturers and twenty-eight learners from the English Language Education and the English Literature study program at one of the prominent universities in Malang. At first, both learners and lecturers created a temporary account on the web prototype, and they were assigned two different roles: learners wrote and submitted a short argumentative essay on the application, and lecturers provided feedback on the essays through a personal comment box. At the end of the trial, both groups revealed their experience and inputs on the features, operation accessibility, and web design through questionnaires. To a large extent, both lecturers and learners were satisfied with the goal of the application to provide easy accessibility to assessing academic text. However, both groups agreed that more distinctive features should be added, along with a manual book and language switch feature, as it is attainable that the future users of WISSE will not be limited to EFL learners. The user trial results illustrate that while WISSE needs further development and revision, it exhibits proper performance and is prepared for large-scale use.

The Psychological Significance of the Social Services Situation on Improving Municipal Communication in Social Policy

The paper aimed to show the psychological significance of a functional social system at the local level in the context that a functional network of social services improves the quality of life in a community. The problem was discussed using the case of an analysis of social service providers in a particular municipality. Citizen satisfaction is a key objective of public policy and the goal of every human individual is to move towards satisfaction with his or her life. An accessible social network improves the subjective perception of their problems among residents who find themselves in a difficult social situation. The task of local government is then to provide sufficient social service options to communicate with its citizens. The analysis carried out showed the limits and challenges for the future direction of the social network. This must necessarily mean improving the communication strategy of the municipality as a guarantor of social policy. At the center of this effort is the user of social services or the citizen who is a potential future user.

Assessment of the Success of Digital Signature and Stamp Implementation in the National Economic Rescue (PEN) Application System at Credit Insurance Companies in Indonesia

Utilization technology in sector industry non-bank finance in Indonesia is increasingly widespread, including in the guarantee credit of state-owned companies. The company uses System Application Guarantee for transaction guarantee credit with produce Certificate Guarantee as proof of approval. The request can be active in the context of the COVID-19 pandemic and the Indonesian government's efforts to implement the company's National Economic Recovery (PEN) program guarantee. Implementation feature sign hand and digital stamp on the System Application PEN underwriting allows provision of document Guarantee Certificate in the form of legal and supportive softcopy restrictions contact physique during a pandemic. Study This aim is to measure the successful implementation features of the success models by Delone and McLean and analyze the connection between variables in research. The benefits cover assessment, evaluation, and strategy-based results evaluation using feature sign hand and digital stamp. Delone and McLean's original model was modified to eliminate variable intention to use. The research was conducted by analyzing the results of questionnaires from 108 officials' user systems throughout Indonesia. Process of data analysis using the method of Partial Least Square with device SMARTPLS software. Research results prove that quality systems will influence the satisfaction of future users who will be influential to benefit clean from the feature sign hand and digital stamp on the System Application PEN Guarante.

Evaluation of historical data on persistent organic pollutants and heavy metals in Lake Baikal: Implications for accumulation in marine environments

Lake Baikal, the largest freshwater lake by volume, provides drinking water and aquatic food supplies to over 2.5 million people. However, the lake has been contaminated with recalcitrant pollutants released from surrounding industrial complexes, agriculture, and natural lands, thereby increasing the risk of their bioaccumulation in fish and seals. Yet, a collective analysis of historical concentration data and their bioaccumulation potential as well as what factors drive their accumulation in fish or seals remains largely unknown. We analyzed concentration data from 42 studies collected between 1985 and 2019 in water, sediment, fish, and seals of Lake Baikal. Heavy metals had the highest concentrations in water and biota followed closely by polycyclic aromatic hydrocarbons (PAHs) and organochlorines. Among organochlorines, polychlorinated biphenyls (PCBs) showed the highest levels in water, surpassing hexachlorocyclohexane (HCH) concentrations, particularly after normalizing to solubility. While naphthalene and phenanthrene exhibited the highest average concentrations among polycyclic aromatic hydrocarbons (PAHs), their relative concentrations significantly decreased upon solubility normalization. The analysis confirmed that bioconcentration and biomagnification of organochlorine pesticides, PCBs, PAHs, and heavy metals depend primarily on source strength to drive their concentration in water and secondarily on their chemical characteristics as evidenced by the higher concentrations of low-solubility PCBs and high molecular weight PAHs in water and sediment. The differential biomagnification patterns of Cu, Hg, and Zn compared to Pb are attributed to their distinct sources and bioavailability, with Cu, Hg, and Zn showing more pronounced biomagnification due to prolonged industrial release, in contrast to the declining Pb levels. Dibenzo-p-dioxins were detected in sediment and seals, but not in water or fish compartments. These data highlight the importance of addressing even low concentrations of organic and inorganic pollutants and the need for more consistent and frequent monitoring to ensure the future usability of this and other similar essential natural resources.

Thiazole derivatives: prospectives and biological applications

Thiazole derivatives have long been a hot topic in pharmaceutical research and remain among the greatest active fields in heterocyclic chemistry. Thiazole derivatives, as one of the potentially favored structure, have been extensively desirable by industrial and medicinal researchers and have gained significant success in the previous decades due to their various biological activities, such as anticancer, antibacterial, antifungal, anti-HIV, antiulcer, and anti-inflammatory activity. In addition, many thiazole drugs are well-known pharmaceuticals on the market. This review summarizes the freshly synthesized routes and prospective biological activities of thiazole derivatives. In addition, it highlights thiazoles as treatment drugs in clinical trials or approved by the FDA and spotlights on some of their industrial applications. On the other hand, one of the goals of this review is to open up prospects for the future design, development, and usage of thiazole derivatives as potent drugs.

Weight Adjustment Framework for Self-Attention Sequential Recommendation

In recent years, sequential recommendation systems have become a hot topic in the field of recommendation system research. These systems predict future user actions or preferences by analyzing their historical interaction sequences, such as browsing history and purchase records, and then recommend items that users may be interested in. Among various sequential recommendation algorithms, those based on the Transformer model have become a focus of research due to their powerful self-attention mechanisms. However, one of the main challenges faced by sequential recommendation systems is the noise present in the input data, such as erroneous clicks and incidental browsing. This noise can disrupt the model’s accurate allocation of attention weights, thereby affecting the accuracy and personalization of the recommendation results. To address this issue, we propose a novel method named “weight adjustment framework for self-attention sequential recommendation” (WAF-SR). WAF-SR mitigates the negative impact of noise on the accuracy of the attention layer weight distribution by improving the quality of the input data. Furthermore, WAF-SR enhances the model’s understanding of user behavior by simulating the uncertainty of user preferences, allowing for a more precise distribution of attention weights during the training process. Finally, a series of experiments demonstrate the effectiveness of the WAF-SR in enhancing the performance of sequential recommendation systems.

Shock compression experiments using the DiPOLE 100-X laser on the high energy density instrument at the European x-ray free electron laser: Quantitative structural analysis of liquid Sn

X-ray free electron laser (XFEL) sources coupled to high-power laser systems offer an avenue to study the structural dynamics of materials at extreme pressures and temperatures. The recent commissioning of the DiPOLE 100-X laser on the high energy density (HED) instrument at the European XFEL represents the state-of-the-art in combining x-ray diffraction with laser compression, allowing for compressed materials to be probed in unprecedented detail. Here, we report quantitative structural measurements of molten Sn compressed to 85(5) GPa and ∼3500 K. The capabilities of the HED instrument enable liquid density measurements with an uncertainty of ∼1% at conditions which are extremely challenging to reach via static compression methods. We discuss best practices for conducting liquid diffraction dynamic compression experiments and the necessary intensity corrections which allow for accurate quantitative analysis. We also provide a polyimide ablation pressure vs input laser energy for the DiPOLE 100-X drive laser which will serve future users of the HED instrument.

AI-Based Facial Emotion Recognition

Facial expression, age, and gender detection have received a lot of interest in recent years due to their wide range of applications in fields including healthcare, security, marketing, and entertainment. With the proliferation of artificial intelligence (AI) techniques, especially deep learning, facial analysis systems' accuracy and efficiency have improved significantly. This paper provides a comprehensive analysis of the most recent advances in AI-based facial expression, age, and gender identification algorithms. We explore the constraints and limitations of current techniques, such as the necessity for big annotated datasets, biases in training data, and interpretability issues in deep learning models. This study seeks to give academics, practitioners, and policymakers, a thorough grasp of the most recent state-of-the-art methodologies and trends in AI-based facial emotion, age, and gender identification, thereby supporting future advancements and responsible usage of these technologies. Keywords: Facial expression, age, gender, identification, deep learning, artificial intelligence

Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach

The aim of this research is to address the challenge of transforming car-oriented industrial parks into pedestrian- and bicycle-friendly environments. Through the implementation of a multi-criteria decision-making (MCDM) approach, the study aims to evaluate alternative pathway connections and assess their potential impact on bicycle and pedestrian traffic volumes. By enhancing the connectivity of the cycling pathway network, the research seeks to demonstrate the potential for substantial increases in cycling and walking within industrial zones. This research leverages a multi-criteria decision-making framework, specifically the ARAS-G method, and integrates geographic information system analysis alongside Python scripting to project future bicycle usage and assess alternative pathway connections. The study underscores the potential for substantial increases in cycling and walking by augmenting the connectivity of the cycling pathway network. The findings hold practical significance for urban planners and industrial zone developers, advocating a holistic approach to sustainable transportation. The research contributes a comprehensive set of criteria encompassing connectivity, safety, accessibility, efficiency, integration within the urban fabric, and cost-effectiveness to evaluate sustainability and prioritize actions and measures for reestablishing industrial zones as bicycle-friendly spaces.

Evolution of cosmic filaments in the MTNG simulation

We present a study of the evolution of cosmic filaments across redshift with an emphasis on some important properties: filament lengths, growth rates, and radial profiles of galaxy densities. Following an observation-driven approach, we built cosmic filament catalogues at z = 0, 1, 2, 3, and 4 from the galaxy distributions of the large hydro-dynamical run of the MilleniumTNG project. We employed the extensively used DisPerSE cosmic web finder code, for which we provide a user-friendly guide, including the details of a physics-driven calibration procedure, with the hope of helping future users. We performed the first statistical measurements of the evolution of connectivity in a large-scale simulation, finding that the connectivity of cosmic nodes (defined as the number of filaments attached) globally decreases from early to late times. The study of cosmic filaments in proper coordinates reveals that filaments grow in length and radial extent, as expected from large-scale structures in an expanding Universe. But the most interesting results arise once the Hubble flow is factored out. We find remarkably stable comoving filament length functions and over-density profiles, showing only little evolution of the total population of filaments in the past ∼12.25 Gyr. However, by tracking the spatial evolution of individual structures, we demonstrate that filaments of different lengths actually follow different evolutionary paths. While short filaments preferentially contract, long filaments expand along their longitudinal direction with growth rates that are the highest in the early, matter-dominated Universe. Filament diversity at a fixed redshift is also shown by the different (∼5σ) density values between the shortest and longest filaments. Our results hint that cosmic filaments can be used as additional probes for dark energy, but further theoretical work is still needed.