Category: 4. Technology

Samsung is taking background noise cancellation to the next level. With the upcoming One UI 8.0 update, the Audio Eraser feature is getting faster and expanding beyond the Gallery app. Previously, Audio Eraser was limited to the Gallery in One UI 7.0, where it could detect up to six types of audio in videos and let users adjust their volume manually. However, the process involved tapping a Galaxy AI button and entering a separate editing screen, making it a bit slow and clunky.

Now, One UI 8.0 changes that. Samsung has not only improved the speed of Audio Eraser but also integrated it into more stock apps, including Samsung Notes and Voice Recorder. When you record audio in either app, a Galaxy AI button appears below the clip. Just tap it, and the app will instantly remove background noise.

This enhancement means users can now clean up voice memos or lecture recordings with a single tap, making note-taking and audio capture much clearer.

In the Gallery app, things are now more seamless too. Instead of opening a dedicated screen, users can just tap the Audio Eraser icon in the corner while playing a video. The noise reduction happens instantly.

This improved version of Audio Eraser has appeared in the latest internal beta build of One UI 8.0 for the Galaxy S25 series. It’s not yet part of the public beta, but sources suggest it will be included in the next public beta release for supported Galaxy devices.

As Samsung continues to integrate Galaxy AI deeper into everyday apps, features like Audio Eraser are becoming more practical and accessible.

Also read:

Samsung’s Galaxy AI Wants to Be More Than Just a Tool—It Wants to Know You

Introduction

Chronic obstructive pulmonary disease (COPD) is a major public health concern, as it remains one of the leading causes of death worldwide.¹ Patients with COPD often experience reduced musculoskeletal mass due to chronic inflammation, malnutrition, and inactivity resulting from dyspnea.^2,3 This musculoskeletal loss contributes to sarcopenia and osteoporosis, increasing fracture risk and further inactivity, which worsens prognosis.^4,5 Exercise plays a vital role in maintaining healthy body composition.⁶ The Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2025 report emphasizes the importance of pulmonary rehabilitation.⁷ However, its availability is limited due to high costs and a shortage of therapists.⁸ Encouraging patients with COPD to establish their exercise habits may promote physical activity (PA) and help reduce healthcare costs.

Musculoskeletal mass reaches a peak in young adulthood and gradually declines with age.⁹ Higher levels of PA during youth are associated with increased lean mass, and maintaining PA in later life helps preserve it.⁶ However, to the best of our knowledge, no study has clarified the associations of adolescent versus current exercise habits on body composition in patients with COPD. Moreover, the association of adolescent exercise on current PA levels and pulmonary function in patients with COPD has not been thoroughly investigated.

We hypothesized that adolescent and current exercise habits would independently contribute to an improved clinical profile of COPD. In the present study, we aimed to clarify the associations of adolescent and current exercise habits with body composition, PA, and pulmonary function. Furthermore, we determined whether disease severity influences these effects.

Materials and Methods

Participants

Outpatients with COPD or pre-COPD at our university hospital between October 2021 and December 2023 were enrolled in this cross-sectional study, as part of exploratory research. Participants with a smoking history of more than 10 pack-years and no exacerbation of respiratory symptoms within 4 weeks prior to enrollment were included. The exclusion criteria included severe heart failure, progressive malignant diseases, or other chronic pulmonary diseases, except for stable asthma. COPD and pre-COPD were diagnosed according to the GOLD recommendations.⁷ In this study, Pre-COPD and GOLD 1 were defined as mild COPD (forced expiratory volume in 1 s [FEV₁] % predicted ≥ 80%), and GOLD 2 to 4 were defined as moderate-to-severe COPD (FEV₁ % predicted < 80%). This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. This study was approved by the Ethics Committee of Shiga University of Medical Science (registration number: R2021-026), and all participants provided written informed consent before participation.

Exercise Habit Questionnaire

Self-report questionnaires were used to ask the participants about their exercise habits at different stages of their lives (Figure S1). We defined exercise habit as engaging in any sport or exercise at least twice a week for a minimum of 30 min per session.¹⁰ Participants were categorized as “adolescent exercisers” if they had exercise habits between the ages of 16 and 22 years for at least 1 continuous year,¹¹ and as “current exercisers” if they had maintained exercise habits for at least 1 year.

PA Assessment

PA was measured using a triaxial accelerometer (Active Style Pro HJA-750C; Omron Healthcare, Kyoto, Japan). The participants wore the device during the daytime, except while bathing, for 14 days. Rainy days were excluded, and the data were validated as previously described.¹² The mean durations of PA based on metabolic equivalents (METs) and step counts were analyzed. The intensity of PA was defined as moderate to vigorous PA (moderate to vigorous physical activity [MVPA] ≥ 3.0 METs), light PA (1.6 to 2.9 METs), and sedentary behavior (1.0 to 1.5 METs), based on a previous report.¹³

Body Composition Analysis

Body composition was assessed using direct segmental multifrequency bioelectrical impedance analysis (BIA) (InBody S10; InBody Co., Ltd., Seoul, South Korea).⁶ Multifrequency measurements were performed for each body segment in the supine position. Fat-free mass index (FFMI) and fat mass index (FMI) were calculated by dividing the respective mass values by height squared. Phase angle (PhA), a raw BIA variable, reflects the relationship between reactance and resistance in the body as a conductor, and a smaller value indicates a worse cellular condition.¹⁴ The PhA at 50 kHz provides information about muscle quality.^14,15

Handgrip Strength Test

Handgrip strength was measured twice per hand, and the maximum value was analyzed.

6-minute Walk Test

A 6-minute walk test was performed according to the guidelines.¹⁶

Pulmonary Function Tests

Pulmonary function tests were performed after the inhalation of 20 μg procaterol using a spirometer (FUDAC77; Fukuda Denshi, Tokyo, Japan), according to the ATS/ERS guidelines.¹⁷ Carbon monoxide diffusing capacity was measured using the single-breath washout technique. Predicted spirometry values were calculated according to the Japanese Respiratory Society guidelines.¹⁸

Computed Tomography Imaging

Chest computed tomography (CT) was performed in the supine position using a 320-detector row CT scanner (Aquilion ONE; Canon Medical Systems Corporation, Tochigi, Japan) with full inspiration. The 20 μg procaterol inhalation was given 1 hour before CT scan. Emphysematous lesions were assessed as the percentage of low attenuation volume (LAV%), which is defined as the percentage of lung volume exhibiting CT attenuation values below –950 Hounsfield units. Small airway lesions were assessed by plotting the square root of the wall area (√Aaw) of each visible bronchial segment against its internal perimeter, and estimating the √Aaw for a hypothetical airway with an internal perimeter of 10 mm using linear regression (√Aaw at Pi10). All parameters were quantified using the Apollo software version 1.2 (VIDA, Coralville, IA, USA), based on previous reports.^19,20

Statistical Analysis

Statistical analyses were performed using JMP Pro 17 software (SAS Institute, Cary, NC, USA). Differences between exercisers and non-exercisers were evaluated using the Wilcoxon rank-sum and Fisher’s exact tests. The correlations between PA and body composition were assessed using Spearman’s rank correlation coefficients. Statistical significance was set at p < 0.05.

Results

A total of 86 participants (81 men and 5 women) were enrolled in this study. Seventy-two patients were diagnosed with COPD, and 36 patients were diagnosed with moderate-to-severe COPD (Table 1). As presented in Figure S2, there was no relationship between the presence or absence of adolescent exercise habits and current exercise habits. Adolescence and current exercise habits were not related to exercise habits in the 30s to 40s.

Table 1 Patient Characteristics

Association Between Adolescent Exercise Habits and Current Conditions

The demographic and clinical characteristics of the participants were not significantly different between adolescent exercisers and non-exercisers (Table S1). Adolescent exercise habits did not influence step count, PA duration at any intensity, (Figure 1A–D), or body composition parameters (Figure 1E–H). When pulmonary function and CT imaging were determined, only vital capacity was significantly higher in adolescent exercisers than in non-exercisers (p = 0.038; Table 2).

Table 2 Associations of Adolescent Exercise Habits with Pulmonary Functions and CT Imaging Biomarkers

Figure 1 Associations of adolescent exercise habits with physical activity and body composition parameters. (A) Comparison of step counts per day. (B) Comparison of MVPA per day. (C) Comparison of light PA per day. (D) Comparison of sedentary behavior per day. (E) Comparison of fat-free mass index. (F) Comparison of bone mineral content. (G) Comparison of phase angle at 50 kHz. (H) Comparison of fat mass index. Abbreviations: MVPA, moderate to vigorous physical activity; PA, physical activity.

Association Between Current Exercise Habits and Current Conditions

Table S2 presents the demographic and clinical characteristics of current exercisers and non-exercisers. Current non-exercisers were more likely to be current smokers or female sex. They also showed a non-significant trend toward lower grip strength. Current exercisers were more physically active (p < 0.001; Figure 2A, and p < 0.001; Figure 2B) throughout the day. The duration of light PA tended to be longer in current exercisers than in non-exercisers, but this difference was not significant (p = 0.059; Figure 2C). Current exercisers spent less time engaging in sedentary behaviors (p = 0.021; Figure 2D). Body composition measures, including FFMI (p = 0.002; Figure 2E), bone mineral content (BMC, p = 0.009; Figure 2F), and PhA (p = 0.017; Figure 2G), were significantly higher in current exercisers than in non-exercisers, whereas the FMI (p = 0.52; Figure 2H) and body mass index (BMI; p = 0.42; Table S2) showed no significant differences. Multiple linear regression analysis, adjusted for age, sex, FEV₁ % predicted, and both adolescent and current exercise habits, revealed that current exercise habits were independent factors affecting FFMI, BMC, and PhA (Table 3). The results remained consistent when an interaction term between adolescents and their current exercise habits was incorporated into the multivariate analysis (data not shown). Although CT imaging parameters were not related to current exercise habits, diffusing capacity was higher in current exercisers than in non-exercisers (p = 0.006; Table 4).

Table 3 Factors Associated with Body Composition Parameters Based on the Multiple Linear Regression Test

Table 4 Associations of Current Exercise Habits with Pulmonary Functions and CT Imaging Biomarkers

Figure 2 Associations of current exercise habits with physical activity and body composition parameters. *is significant at the P < 0.05 level. (A) Comparison of step counts per day. (B) Comparison of MVPA per day. (C) Comparison of light PA per day. (D) Comparison of sedentary behavior per day. (E) Comparison of fat-free mass index. (F) Comparison of bone mineral content. (G) Comparison of phase angle at 50 kHz. (H) Comparison of fat mass index. Abbreviations: MVPA, moderate to vigorous physical activity; PA, physical activity.

Associations Between MVPA per Day and Body Composition Parameters by COPD Severity

As presented in Table 5 and Figure 3, MVPA per day was positively correlated with the FFMI and PhA, especially in moderate-to-severe COPD (rho = 0.51, p = 0.003; rho = 0.45, p = 0.011, respectively). In contrast, in mild COPD, MVPA per day was associated with the PhA and FMI (rho = 0.32, p = 0.024; rho = −0.29, p = 0.042, respectively).

Table 5 Associations Between MVPA per Day and Body Composition Parameters by COPD Severity

Figure 3 Correlations between MVPA per day and muscle quantity or quality indicators in moderate-to-severe COPD. (A) Correlation between MVPA per day and fat-free mass index. (B) Correlation between MVPA per day and phase angle at 50 kHz. Abbreviation: MVPA, moderate to vigorous physical activity.

Discussion

In patients with COPD, adolescent exercise habits showed no significant association with daily PA levels or body composition, although they were associated with increased lung volume. In contrast, current exercise habits were associated with prolonged engagement in higher PA, with a reduction in sedentary behavior, improved body composition, and enhanced diffusing capacity. In addition, the correlation between PA and musculoskeletal parameters varied with COPD severity and was more pronounced in patients with moderate-to-severe COPD than in those with mild COPD.

Previous studies have suggested that in healthy older adults, youth sports participation is associated with elevated PA levels and improved body composition in later life.^21,22 Contrary to our hypothesis, adolescent exercisers neither showed a tendency to maintain a high level of PA nor a better body composition in later life. Prolonged chronic inflammation, disease-related inactivity, particularly due to breathlessness, and COPD comorbidities may negate any long-term benefits of adolescent exercise.^2,5 Although adolescent exercise habits did not significantly affect COPD features, a notable finding was a higher lung volume in adolescent exercisers than in non-exercisers. Our results align with those of earlier studies in healthy adults;^23,24 however, to the best of our knowledge, our study is the first to describe this association in patients with COPD. Although the mechanisms underlying lung development through exercise are not fully understood, regular exercise during adolescence may be associated with lung development.^23,24 As impaired lung development reportedly contributes to the future onset of obstructive lung diseases, early life exercise may play a beneficial role in preventing the development or progression of COPD.²⁵

In the present study, current exercisers were physically active regularly and did not exhibit sedentary behaviors such as prolonged periods of television viewing. Increased PA is associated with improved body composition in COPD.^26,27 Consistently, our study demonstrated that current exercisers had significantly higher musculoskeletal measures than non-exercisers. Furthermore, these associations persisted even after adjusting for confounders, including adolescent exercise habits. It is possible that patients with well-controlled COPD are more likely to maintain exercise routines. However, regular exercise, even after the onset of COPD, may be more important than exercise during adolescence for achieving optimal body composition, leading to a good prognosis and indirectly contributing to reduced healthcare costs.⁴ Moreover, a low level of PA is associated with muscle wasting and reduced exercise performance.²⁷ Although the difference in grip strength was not statistically significant—possibly due to the relatively small sample size—grip strength, a reported prognostic factor, tended to be higher in current exercisers.^28,29

Diffusing capacity was significantly higher in current exercisers, which is consistent with the findings of a previous observational study.³⁰ However, there were no statistically significant differences in CT imaging factors. Individuals with a higher diffusing capacity may engage in higher levels of PA. However, regular exercise may promote pulmonary circulation at the capillary level and prevent a decrease in diffusing capacity,³⁰ regardless of morphological changes. Further studies are required to investigate these causal relationships.

In addition, we observed correlations between the duration of MVPA and musculoskeletal parameters in patients with moderate-to-severe COPD, consistent with a previous study.³¹ Patients with more severe COPD reportedly have lower musculoskeletal mass and experience faster muscle loss than those with mild disease.^2,3,20 Notably, exercise-induced changes in the muscle are not impaired in patients with severe COPD, and exercise may have an even greater impact on body composition in this population.³² Our finding further suggests that maintaining daily PA levels is crucial for preventing musculoskeletal mass loss, particularly in patients with more severe COPD.

This study had some limitations. This was a single-center study conducted in Japan with a relatively small sample size. As a cross-sectional study, it cannot establish causal relationships, and recall bias may have occurred. Moreover, nutritional supplementation, which may be related to body composition, was not evaluated. However, no participant reported anorexia at the time of examination. Further prospective studies are needed to confirm our findings and explore the mechanisms underlying these associations.

Conclusion

Exercise during adolescence may be associated with increased lung volume. However, even after the onset of COPD, regular exercise routines can help maintain PA, improve body composition, and diffusing capacity, particularly if the disease has progressed. The results of this study effectively underscore the importance of exercise habits in patients with COPD.

Acknowledgments

The authors would like to thank Yasutaka Horii, Yukie Miyatake, and Yoko Naito for their assistance throughout this study.

Disclosure

The authors report no conflicts of interest in this work.

References

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2. Jin X, Yang Y, Chen G, et al. Correlation between body composition and disease severity in patients with chronic obstructive pulmonary disease. Front Med Lausanne. 2024;11:1304384. doi:10.3389/fmed.2024.1304384

3. Watanabe K, Onoue A, Kubota K, et al. Association between airflow limitation severity and reduced bone mineral density in Japanese men. Int J Chron Obstruct Pulmon Dis. 2019;14:2355–2363. doi:10.2147/COPD.S213746

4. Vestbo J, Prescott E, Almdal T, et al. Body mass, fat-free body mass, and prognosis in patients with chronic obstructive pulmonary disease from a random population sample: findings from the Copenhagen City heart study. Am J Respir Crit Care Med. 2006;173(1):79–83. doi:10.1164/rccm.200506-969OC

5. Lehouck A, Boonen S, Decramer M, Janssens W. COPD, bone metabolism, and osteoporosis. Chest. 2011;139(3):648–657. doi:10.1378/chest.10-1427

6. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16–31. doi:10.1093/ageing/afy169

7. Global strategy for the diagnosis, management, and prevention of obstructive pulmonary disease (2025 report) [homepage on the Internet]. Global Initiative for Chronic Obstructive Lung Disease; 2025. Available from: https://goldcopd.org/2025-gold-report/. Accessed June 25, 2025.

8. Choi JY, Kim KU, Kim DK, et al. Pulmonary rehabilitation is associated with decreased exacerbation and mortality in patients with COPD: a Nationwide Korean study. Chest. 2024;165(2):313–322. doi:10.1016/j.chest.2023.09.026

9. Sayer AA, Syddall H, Martin H, Patel H, Baylis D, Cooper C. The developmental origins of sarcopenia. J Nutr Health Aging. 2008;12(7):427–432. doi:10.1007/BF02982703

10. Active guide Japanese official physical activity guidelines for health promotion. [homepage on the Internet]. Ministry of Health, Labour and Welfare. 2013. Available from: https://www.nibiohn.go.jp/eiken/programs/pdf/active2013-e.pdf. Accessed June 25, 2025.

11. Conroy MB, Cook NR, Manson JE, Buring JE, Lee IM. Past physical activity, current physical activity, and risk of coronary heart disease. Med Sci Sports Exerc. 2005;37(8):1251–1256. doi:10.1249/01.mss.0000174882.60971.7f

12. Miyamoto S, Minakata Y, Azuma Y, et al. Verification of a motion sensor for evaluating physical activity in COPD patients. Can Respir J. 2018;2018:8343705. doi:10.1155/2018/8343705

13. Cavalheri V, Straker L, Gucciardi DF, Gardiner PA, Hill K . Changing physical activity and sedentary behaviour in people with COPD. Respirology. 2016;21(3):419–426. doi:10.1111/resp.12680

14. Baumgartner RN, Chumlea WC, Roche AF. Bioelectric impedance phase angle and body composition. Am J Clin Nutr. 1988;48(1):16–23. doi:10.1093/ajcn/48.1.16

15. Hamada R, Tanabe N, Oshima Y, et al. Phase angle measured by bioelectrical impedance analysis in patients with chronic obstructive pulmonary disease: associations with physical inactivity and frailty. Respir Med. 2024;233:107778. doi:10.1016/j.rmed.2024.107778

16. Holland AE, Spruit MA, Troosters T, et al. An official European respiratory society/American thoracic society technical standard: field walking tests in chronic respiratory disease. Eur Respir J. 2014;44(6):1428–1446. doi:10.1183/09031936.00150314

17. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J. 2005;26(2):319–338. doi:10.1183/09031936.05.00034805

18. Sasaki H, Nakamura M, Kida K, et al. Reference values for spirogram and blood gas analysis in Japanese adults. J Jpn Respir Soc. 2001;39:S1–S17.

19. Nakano Y, Wong JC, de Jong PA, et al. The prediction of small airway dimensions using computed tomography. Am J Respir Crit Care Med. 2005;171(2):142–146. doi:10.1164/rccm.200407-874OC

20. Yamazaki A, Kinose D, Kawashima S, et al. Predictors of longitudinal changes in body weight, muscle and fat in patients with and ever-smokers at risk of COPD. Respirology. 2023;28(9):851–859. doi:10.1111/resp.14537

21. Teraž K, Kalc M, Šimunič B, et al. Participation in youth sports influences sarcopenia parameters in older adults. PeerJ. 2023;11:e16432. doi:10.7717/peerj.16432

22. Tanaka T, Kawahara T, Aono H, et al. A comparison of sarcopenia prevalence between former Tokyo 1964 Olympic athletes and general community-dwelling older adults. J Cachexia, Sarcopenia Muscle. 2021;12(2):339–349. doi:10.1002/jcsm.12663

23. Hancox RJ, Rasmussen F. Does physical fitness enhance lung function in children and young adults? Eur Respir J. 2018;51(2):1701374. doi:10.1183/13993003.01374-2017

24. Twisk JW, Staal BJ, Brinkman MN, Kemper HC, van Mechelen W. Tracking of lung function parameters and the longitudinal relationship with lifestyle. Eur Respir J. 1998;12(3):627–634. doi:10.1183/09031936.98.12030627

25. Hopkinson NS, Bush A, Allinson JP, Faner R, Zar HJ, Agustí A. Early life exposures and the development of COPD across the life course. Am J Respir Crit Care Med. 2024;210(5):572–580. doi:10.1164/rccm.202402-0432PP

26. Liu WT, Kuo HP, Liao TH, et al. Low bone mineral density in COPD patients with osteoporosis is related to low daily physical activity and high COPD assessment test scores. Int J Chron Obstruct Pulmon Dis. 2015;10:1737–1744. doi:10.2147/COPD.S87110

27. Furlanetto KC, Pinto IF, Sant’Anna T, Hernandes NA, Pitta F. Profile of patients with chronic obstructive pulmonary disease classified as physically active and inactive according to different thresholds of physical activity in daily life. Braz J Phys Ther. 2016;20(6):517–524. doi:10.1590/bjpt-rbf.2014.0185

28. Leong DP, Teo KK, Rangarajan S, et al. Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet. 2015;386(9990):266–273. doi:10.1016/S0140-6736(14)62000-6

29. Kyomoto Y, Asai K, Yamada K, et al. Handgrip strength measurement in patients with chronic obstructive pulmonary disease: possible predictor of exercise capacity. Respir Investig. 2019;57(5):499–505. doi:10.1016/j.resinv.2019.03.014

30. Garcia-Aymerich J, Serra I, Gómez FP, et al. Physical activity and clinical and functional status in COPD. Chest. 2009;136(1):62–70. doi:10.1378/chest.08-2532

31. Yoshimura K, Sato S, Muro S, et al. Interdependence of physical inactivity, loss of muscle mass and low dietary intake: extrapulmonary manifestations in older chronic obstructive pulmonary disease patients. Geriatr Gerontol Int. 2018;18(1):88–94. doi:10.1111/ggi.13146

32. Mølmen KS, Hammarström D, Falch GS, et al. Chronic obstructive pulmonary disease does not impair responses to resistance training. J Transl Med. 2021;19(1):292. doi:10.1186/s12967-021-02969-1

Mecha BREAK, a sci-fi mecha shooter game developed by Chinese studio Seasun Games, launched globally on Wednesday across PC, PlayStation 5, and Xbox platforms. Touted as a beacon of hope for AAA-quality Chinese mecha games, the title saw a peak of over 130,000 concurrent players on Steam in the past two days.

Despite (or perhaps because of) the hype, Mecha BREAK has received a tepid reception from players so far, holding a mixed rating on Steam with over 6,000 reviews and a modest 63% approval rate.

Diverse mecha designs and gameplay modes

Set in a near-future world ravaged by the carbon-silicon substance EIC, Mecha BREAK follows elite mech pilots fighting to save humanity from an escalating existential threat. The game features three core gameplay modes: 6v6 Edge Battlefield (strategy-focused team combat), 3v3 Ace Sequence (death-match), and PvPvE Marsh Mark (loot-and-extract survival mode).

Mecha BREAK is free-to-play but offers in-game purchases for game skins, season passes, gears, extra bonuses, and other premium content. The current version offers 12 free mechs. They are divided into five roles: assault, melee, sniper, defense, and support. Each mech also falls into a weight class of light, medium, or heavy, which affects its movement speed, armor durability, and skill cooldowns.

UI issues disrupt the experience

Many players on Steam have criticized the user interface, describing it as cluttered, confusing, and poorly organized. Key functions are buried in deep menu layers, while overlapping prompts create an overwhelming experience, especially for first-time players.

Poor color contrast and low icon recognizability, combined with interaction logic that ignores typical PC game conventions, have led some players to complain that the game “feels like a mobile UI ported directly to PC.” 

Monetization discomfort and unsatisfying combat feedback

Early Steam reviews have also voiced strong dissatisfaction with the game’s monetization approach, particularly the instant pop-up of a RMB 288 ($40) limited-time offer immediately after the tutorial. Some players argued that the early emphasis on spending detracts from the gameplay experience and breaks immersion.

In an interview with TechNode, an online gamer known as Phantom Core criticized the game’s combat compared to titles such as Armored Core VI. He described the hit feedback as “plastic”, saying that the sound and visual effects are not properly matched and that the attack impacts are underwhelming. 

Core gameplay balance faces questions

The game’s 6v6 battlefield mode has drawn criticism for balance issues. Steam players report a clear disparity in mech performance, which makes fair competition difficult. Heavier defense-focused mechs offer disproportionately high firepower and survivability, whereas lighter units intended as assassins are under-powered and poorly tuned, Phantom Core said.

The PvPvE (Player vs Player vs Environment) mode also brought complaints on Steam for resource imbalances. Players who invest more time or money can quickly power up their mechs via boss drops and lootable upgrades, while average players fall behind in progression. This system translates directly into PvP combat power gaps, leading to a “grind (or spend) more, win more” experience that widens the divide between veteran and new players, Phantom Core explained.

Can Mecha BREAK defy the drop?

Despite ongoing controversy around the title, the development team is expected to continue refining the gameplay and system mechanics in response to player feedback. Whether the game can break away from the common pattern of early hype followed by rapid decline and disappointment remains to be seen.

Related

Sergey Kharitonov, whose work on small-scale liquid simulations we’ve featured before, has presented a new water simulation setup and shared insights into his approach.

Adding water droplets to close-up renders is a popular technique for boosting visual interest and making objects appear more dynamic and detailed. While creating static droplets on a surface is relatively straightforward, even for beginners, animating them to move realistically across surfaces is a much more complex challenge.

As Sergey mentioned, he personally considers two existing methods to be among the most realistic: a procedural tool developed by José Mauro Lobão and an X-Particles rig for Cinema4D created by Sam Tato. There’s also a built-in Houdini shelf tool located under Particle Fluids – Condensation, but he finds it relatively slow and difficult to control, likely due to its reliance on the FLIP solver at its core. The artist decided to take on the challenge and build a version entirely from scratch. Here’s the algorithm he followed:

Valve’s annual Steam Summer Sale is live and PC gamers can now take advantage of some of the deepest discounts of the year.

From critically acclaimed RPGs to blockbuster shooters and fan-favourite adventures, this year’s sale features major titles across different genres – with many up to 80% off.

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Rockstar’s sweeping western remains one of the most immersive story-driven games in recent memory.

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Whether you’re building your backlog or finally grabbing a title you’ve waited months to try, the 2025 Steam Summer Sale has something for everyone.

The sale ends Thursday, July 10 at 10am Pacific Time. Players can access deals through the Steam client or at store.steampowered.com.

The Fraunhofer Institute for Electron Beam and Plasma Technology (FEP) sees its results as “valuable basis for optimizing lithium-ion batteries” in industry. According to Fraunhofer FEP, the technology developed by its researchers enables “the precise application of copper and aluminum layers onto polymer films to produce current collectors with electrical conductivity and thickness comparable to conventional metal foil-based current collectors.” While current collectors are also simply referred to as electrodes, Fraunhofer FEP consistently uses the term ‘current collectors’ in its announcement.

Battery electrodes are produced by coating the active materials (such as a lithium, nickel, manganese, cobalt and binder mixture in an NMC cathode) onto a thin metal foil. This metal foil – typically aluminium for the cathode and copper for the anode – serves as the carrier, while the electrochemical reaction for energy storage takes place on the coating surface. The innovation here focuses precisely on replacing these carrier foils inside the electrodes.

The research team in Dresden has substituted the pure metal foil with a polymer film coated on both sides with a thin layer of aluminium or copper – offering similar conductivity properties to pure metal foils. Both copper and aluminium coatings are around one micrometre thick. According to Fraunhofer FEP, the coated polymer films remained “free of significant wrinkling – ideal for further processing in battery production.” The coatings themselves were applied using electron beam evaporation.

“The challenge was to design the polymer films and the coating process in such a way that the thickness of the current collector could be comparable to that of current metal films and the metal layer could have optimum electrical conductivity,” says technical project manager Claus Luber. The team succeeded and demonstrated the deposition of thick copper and aluminium layers onto 12-micrometre PET films. Deposition was carried out in a roll-to-roll process on web widths up to 60 centimetres.

Polymer films offer two main advantages. Firstly, they are lighter than pure metal foils due to the ultra-thin metal coating of just one micrometre per side. This reduces electrode weight and increases the battery cell’s energy density. More importantly, however, is the safety aspect. Should an internal short circuit occur, the polymer substrate melts, interrupting the current path. “ This prevents heat from continuing to build up and causing thermal runaway,” explain the researchers.

In the PolySafe project funded by Germany’s Federal Ministry of Education and Research (BMBF), the team demonstrated not only the production process but also a functional cell based on these electrodes. Project partner TU Braunschweig manufactured pouch cells using the metal-polymer current collectors. “These cells were tested for their electrochemical properties and compared with conventional reference cells. In these tests, the cells with metal-on-polymer current collectors performed similarly to the reference cells in terms of performance and cycle stability at different charging and discharging rates,” reports Fraunhofer FEP.

fraunhofer.de

The next generation of iPhone software, iOS 26, is expected to drop this fall. There are a lot of exciting new updates to be on the lookout for, including the new Liquid Glass design. But if you’re looking to upgrade your iPhone user experience in the meantime, clearing your browser cache is one important kind of digital maintenance.

Clearing your cache gives Safari and other web browsers a clean slate. It can improve performance, free up space and help fix annoying bugs like slow page loads or broken links. It’s not an entire system overhaul, but it can help keep things moving while you’re scrolling online. If it’s been a while since you gave your iPhone’s browser a reset, here’s how to clear your cache and give your device a quick refresh.

Your browser cache is kind of like a digital mini fridge. Whether you use Chrome, Safari, Firefox or another browser, the cache stores page elements locally to speed up your next visit. It’s great for grabbing a digital snack or drink without having to trek all the way to the kitchen — at least, until the mini fridge gets overstocked with expired food, which in this analogy is outdated page elements. 

Clearing your cache is the equivalent of cleaning out that mini fridge. You’ll have to restock it (your first visit after you clear your iPhone cache might be a bit slower), but once you do, you’ll have a better experience than before.

What happens when you clear your cache?

Clearing your cache usually involves clearing website cookies, too. Cookies work similarly to browser caches, except they store information about user data, rather than data on the website itself. Clearing your cookies gives you a chance to reset those preferences, which could improve your browsing experience. Note that clearing your cache and cookies will log you out of sites, which means you’ll have to log into them again and reset any preferences. The up-front investment of that time can lead to a smoother experience down the road and it can be a useful fix if you’ve recently changed settings that aren’t being applied properly.

Here are step-by-step guides on how to clear your iPhone’s cache, depending on the browser you prefer.

How to clear your iPhone cache in Safari

Safari is the default browser on iPhones and you can clear your Safari cache in a few short steps. Starting with iOS 11, following this process will affect all devices signed in to your iCloud account. As a result, all of your devices’ caches will be cleared and you’ll need to sign in to everything the next time you use them. Here’s what to do.

1. Open the Settings app on your iPhone.

2. Select Apps > Safari.

3. Scroll down to History and Website Data and choose Clear History and Website Data.

4. Choose Clear History and Data in the pop-up box — you can choose anywhere from the last hour to all history.

Then you’re set!

How to clear your iPhone cache in Chrome

Chrome is another popular browser for iPhone users. Google has simplified the process for clearing your Chrome cache, making it much faster to clear your data.

1. Open the Chrome app.

2. Select the three dots in the bottom right to open more options.

3. Swipe over to Settings in the top menu bar. 

4. Scroll down and tap Privacy and security.

5. Tap Delete browsing data to open another menu. Select the intended time range at the top of the menu (anywhere from Last 15 minutes to All time). Make sure that Cookies and Site Data are checked, along with Cached Images and Files. Finally, hit Delete data at the bottom of the screen.

How to clear your iPhone cache in Firefox

If you’re a Firefox devotee, don’t worry. Clearing the cache on your iPhone is straightforward. Just follow these steps. 

1. Press the hamburger menu in the bottom right corner to open options.

2. Choose Settings at the bottom of the menu.

3. Select Data Management in the Privacy section.

4. To clear everything, choose Web Site Data at the top of the menu and then Clear All Web Site Data. Alternatively, you can choose to clear just your browsing history, cache, cookies, offline web data, tracking protection or offline files.

How often do I need to clear my cache?

Most people only need to clear their caches once every month or two. That’s generally the point when your browser will build up a cache large enough to start slowing things down. If you frequent a large number of sites, you should err on the side of clearing your cache more often.

New data from mobile measurement platform Kochava reveals that artificial intelligence is exposing massive flaws in how app marketers measure success, with nearly half of marketing budgets generating only 7% of actual value. The analysis also shows Apple Search Ads receiving up to 75% more credit than it deserves while other channels are systematically undervalued by billions of dollars.

The findings, presented by Kochava’s Gary Danks at App Promotion Summit London 2025, show that traditional last-touch attribution is creating industry-wide budget misallocation that’s costing companies millions in wasted ad spend.

The $500-per-conversion problem hiding in plain sight

Marketing Mix Modeling (MMM), the AI-powered statistical method that’s rapidly replacing last-click attribution, is revealing uncomfortable truths about where app marketing dollars actually work. Analysis of 16 of Kochava’s North American clients showed that in one case, 47% of a marketing budget generated only 7% of actual value.

“Between point B and point C, you’ve spent $2,000 just to acquire four more new orders. That’s a CPA of $500 for those last few conversions,” Danks explained, walking through real client data that showed how traditional measurement systems miss these efficiency cliffs entirely.

The problem isn’t just academic. When a UK eCommerce client with a $550,000 monthly budget followed MMM recommendations to reallocate spend without increasing total investment, their cost per acquisition dropped 33% within weeks.

Bottom-funnel channels systematically over-credited

The most striking revelation involves Apple Search Ads, which appears 75% less influential under MMM analysis compared to last-touch attribution. The reason? ASA typically serves as the final touchpoint before app installation, artificially inflating its apparent value.

“ASA often gets credit for users who are already on their way. I like to describe it as the movie theater poster. You see it on the way, but your decision was already made before you got to the App Store,” Danks said.

Meanwhile, platforms like Meta, TikTok, and AppLovin are being systematically undervalued by traditional attribution, revealing what Danks calls “a massive opportunity for marketers.”

The pattern holds across both iOS and Android, with Google facing similar over-attribution due to its control of both the Play Store and search results.

MMM for app growth in 2025

Source: App Promotion Summit

AI is transforming measurement from diagnostic to prescriptive

What separates MMM from traditional incrementality testing is its predictive power. While legacy attribution tells you what happened, AI-powered MMM models answer three critical questions: what’s adding incremental value, when do returns start dropping off, and where should you invest next.

“It’s not just retrospective insight, it’s a roadmap based on your KPIs,” Danks emphasized. “Whether you’re optimizing CPA or ROAS or another goal, MMM tells you exactly how much to spend in each channel to maximize efficiency.”

The technology builds cost curves for each marketing channel, using machine learning to analyze historical spend and performance data. This reveals the exact moment when additional investment stops delivering proportional returns — information that’s invisible to traditional attribution methods.

The measurement revolution accelerates

The shift toward MMM represents more than just better math; it’s part of the broader AI transformation of marketing operations. As privacy regulations continue restricting traditional tracking methods, companies are turning to statistical modeling that doesn’t rely on individual user tracking.

“We’re measuring influence, not just what happened last,” Danks explained. “The question to ask yourself is: do you want to be investing in influence, or do you want to be investing in the final touchpoint?”

For an industry that spends billions annually on user acquisition, the implications are staggering. If traditional attribution is systematically misallocating budget across major platforms, the collective waste could represent hundreds of millions in annual overspend on bottom-funnel channels alone.

Sometimes the most transformative change isn’t spending more, it’s spending smarter.

Microsoft has confirmed ongoing issues with Microsoft Forms, marking another outage affecting Microsoft 365 services. The company is actively working on a solution, though the official service health dashboard hasn’t been updated yet.

The company acknowledged the disruption through its official Microsoft 365 Status X account, instructing users to monitor progress via the Microsoft 365 Admin Center with issue ID “FM1109073”. Despite the confirmed problems, Microsoft’s official 365 Service Health Status website continues to display “everything up and running”. Granted, it’s a discrepancy likely to be addressed as the situation evolves.

European impact confirmed

Reports indicate the outage is affecting multiple European countries, including Belgium, Germany, and the Netherlands. This geographic spread suggests the issue may have either a Europe-wide focus or potentially global reach. Microsoft has yet to clarify the exact scope of affected regions.

Growing pattern of service disruptions

This latest outage adds to a concerning pattern of Microsoft service disruptions in recent months. In January, Microsoft’s MFA system experienced problems that prevented users from accessing various Microsoft 365 applications. More recently, Microsoft Teams suffered a file sharing outage, disrupting collaborative work for many organizations.

The frequency of these incidents has raised questions about the reliability of Microsoft’s cloud services, even as individual system availability remains high. Organizations relying on Microsoft 365 for daily operations continue to face periodic disruptions that can significantly impact productivity.

Microsoft Forms functionality

Microsoft Forms serves as an online survey and quiz builder within the Microsoft 365 ecosystem. The platform enables users to create polls, questionnaires, and feedback forms with customizable themes. Data collected through Forms can be analyzed in Excel and shared across Teams and SharePoint, making it a valuable tool for organizational communication and data collection.

The service’s integration with other Microsoft 365 tools makes outages particularly disruptive for organizations that rely on Forms for regular business processes, including employee surveys, customer feedback collection, and training assessments.

Microsoft has not yet provided specific workarounds for the current Forms outage, though the company typically advises users to monitor the Admin Center for real-time updates and resolution timelines.