Valeo Group Valeo Group | 6 Nov, 2025
| 5 min
By integrating multiple functions into a single embedded valve, this innovation enables automakers to improve energy efficiency and reduce heat pump system complexity, thereby supporting sustainability and energy transition goals.
Hemidactylus platyurus is an arboreal species with morphological adaptations that increase surface area (i.e., finger and toe webbing approximately one-third the length of the digits, ventrolateral skin folds on the trunk, and a dorsoventrally flattened tail; [23]). These adaptations likely act as basic airfoils [24] that enhance gliding performance [25] through decreased wing loading [26], although their aerodynamic function has been debated (see [20]). Individuals of H. platyurus have been observed falling with limbs outstretched [20] and documented to glide and have aerial control by using their tail as an inertial appendage to control pitch, roll, and yaw [17, 18, 27].
We used eight (5 male/3 female) adult flat-tailed house-geckos (H. platyurus) (mean body mass: 4.23 ± 0.92 g; mean SVL: 57.2 mm). All animals were acquired in June of 2018 from LLL Reptile. Geckos were housed in large mesh-walled tanks (width: 50.8 cm, length: 50.8 cm, height: 99 cm) within temperature-controlled rooms (24 °C) with a 12:12 h light:dark cycle. Each tank housed 4–5 individuals. All geckos were provided live crickets and water ad libitum. The ambient room temperature in which experiments were conducted was approximately 25 °C. All procedures were approved by the University of California, Berkeley Animal Care and Use Committee.
We used the same equipment and similar procedures as we used in previously published studies [13, 28] (see Fig. 1 in [13] for illustration). In lieu of dropping the lizards from elevated heights (e.g., [19, 25]), we simulated gliding through use of a custom vertical wind tunnel (Wind Generator 01–10, Aerolab, Laurel, MD, USA; see Supplementary Materials). Since air flowing past a relatively stationary animal is aerodynamically equivalent to an animal moving at the same speed through still air, such a simulation provides equivalent ensuing forces in the air (e.g., [15]). This method both reduced risk of animal escape and enhanced our ability to film (see Movies S1-S2). Due to interindividual variation in lizard mass, we adjusted the nominal windspeed for equilibrium gliding to each individual.
We filmed each aerial event at 400 frames per second using three synchronized cameras (HiSpec 1 cameras, Fastec Imaging, San Diego, California, USA); one camera was positioned at dorsal (top; 960 × 990 pixels; 25 mm lens) perspective and two cameras were positioned at lateral perspectives, both of which were elevated ~ 20° above horizontal (front and side; 1024 × 992 and 832 × 872 pixels, respectively; 50 mm lenses) so as to increase visibility of anatomical landmarks. Given the filming speeds, additional tungsten lighting was needed from above and the sides. As in [13] and [28] we calibrated the cameras in pairs by recording and later digitizing checkerboard points via stereo image pairs [29]. We used footage of a polystyrene ball (diameter of 4 cm, mass of 0.034 g) falling vertically through the test arena, along with a horizontal arena edge, to define the global coordinate system.
Immediately prior to each gecko recording, we recorded the individual’s mass, and then applied non-toxic, high-contrast landmarks at relevant anatomical positions to the gecko’s skin (Figs. 1 and 2) with dark eyeliner (Megaliner, Ulta Beauty) or white paint pen (Garden Marker 450, Monami). We thus marked 14 total landmarks (but see Supplemental Materials): midway between the pectoral and pelvic girdle (or midbody), tail tip and on the wrist, elbow, shoulder, ankle, knee, and hip on both the left and right sides.
Postures and measurements of gliding geckos. A Idealized two-dimensional ball-and-stick representation of a gecko (top view). Black circles represent 14 of the 16 tracked points (the point between the eyes and the point midway along tail were not used): (a) left wrist; (b) left elbow; (c) left shoulder; (d) right shoulder; (e) right elbow; (f) right wrist; (g) midbody; (h) left ankle; (j) left knee; (k) left hip; (l) right hip; (m) right knee; (n) right ankle; and (o) tail tip. Sticks represent the relevant segments connecting points of interest. Black dotted arrows indicate anatomical directions. Blue arrows represent measured angles or distances: (i) elbow angle; (ii) shoulder retraction angle; (iii) wrist position; (iv) knee angle; (v) tail angle (frontal); (vi) hip retraction angle. B Idealized two-dimensional ball-and-stick representation of a gecko (cranial view). Blue arrows indicate (vii) roll and (viii) shoulder abduction angle. C Idealized two-dimensional ball-and-stick representation of a gecko (lateral view). Red dotted-lines represent x,y-plane and z-axis. Blue arrows indicate (ix) pitch; (x) body bend; and (xi) tail angle (sagittal)
We recorded glides for each of the 8 individual geckos fifteen times in the wind tunnel (i.e., 120 videos total). The duration of each recording ranged from 1–10 s. To begin a recording, the gecko was released by hand into the airflow at a height of approximately 25 cm above the arena’s floor. The recording was terminated within ~ 5 s of the start of a gliding event (i.e., traversing horizontally across any portion of the test section) or if the gecko halted its glide by either adhering to an arena wall or gripping the arena’s floor. The geckos did not always perform a gliding event or a limb reciprocation behavior of interest, so we only digitized a subset of all recorded sequences.
First, we identified those videos in which the gecko performed a gliding event. Among those videos, we removed any videos whereby the anatomical landmarks were not visible in at least 40% of frames or at the start and end of a gliding event in at least two cameras; a gliding event began when the gecko lost physical contact with the arena’s walls, floor, or with the researcher’s hand, and ended when the gecko regained contact with the arena. We then reexamined each of these gliding events and selected those recordings in which the geckos performed large and reciprocating forelimb motions relevant to our hypothesis. A total of 32 trials met these criteria from which we quantified limb and body motions. Among these 32 trials, points were visible in two cameras for an average of 95.6% frames.
For each of the 32 trials, we identified the frames in which the gliding event occurred, and tracked anatomical landmarks using DLTdv7 point-tracking software in MATLAB (The MathWorks; [30, 31]) in all three cameras. We used a checkerboard calibration routine in MATLAB to calibrate the cameras; the mean reprojection error across all calibrations was 0.818 ± 0.44 mm. We only used two-camera pairs (i.e., front and side, front and top, and side and top) to triangulate the 3D coordinates for analysis because the cameras differed in frame size and two cameras provided sufficient positional characterization of landmarks. For each trial, we selected the camera pair with the lowest amount of landmark occlusion and the smallest digital reprojection error. Using the ‘triangulate’ function in MATLAB, we reconstructed the three-dimensional point coordinates of the digitized landmarks for the corresponding camera pair. We smoothed positional data with a quintic spline smoothing algorithm [32], adjusting the tolerance value for each trial according to fit using the ‘splinetool’ function in MATLAB. We used this function to interpolate positional data for frames in which landmarks were occluded and used the first- and second- derivatives of each spline to approximate the instantaneous velocity and acceleration, respectively [32]. We calculated roll and pitch of the body, as well as (for both the left and right sides of the gecko) the angles of shoulder retraction, shoulder adduction, elbow flexion, hip retraction, hip adduction, knee flexion, and of the tail relative to both the sagittal and frontal plane (summarized here; see Supplementary Materials and Fig. 2 for details) in all 32 trials so as to quantify overall aerial performance.
To characterize body orientations and limb angles through time, we used vectors between relevant landmarks and two virtual planes: (1) the chest plane, which was bounded by the left shoulder, right shoulder, and midbody, and (2) the abdominal plane, which was bounded by the left hip, right hip, and midbody. To calculate body roll, we found the midpoint between the shoulders (i.e., midshoulder) and created a vector between it and the midbody point. We computationally aligned this vector with the global x-axis and measured the angle between the resulting chest plane and horizontal, such that a roll to the gecko’s right was represented by a negative angle, and a roll to the left was positive. Likewise, to calculate body pitch, we aligned the shoulders with the global y-axis and measured the angle between the chest plane and horizontal; positive pitch angle represents nose-up pitch, and negative pitch is a nose-down orientation. Note, however, that the mid-body point was slightly dorsal to the shoulder points, so when the chest plane was parallel to the horizon, the gecko would exhibit a slightly positive pitch (pitch up); alternatively, if the gecko itself were parallel to the horizon, body pitch would be slightly negative. This observation means that pitch values are skewed slightly negative, which introduces a small error in calculations for which the chest plane was used.
We measured body bend at the midbody point by identifying the midpoint between the hips (midhip), projecting it onto the chest plane, and measuring the angle bound by these three points (with its vertex at midbody). We added 180° to this angle such that body bend angles less than 180° represent a flexed vertebral column (i.e., with hips below the chest plane), and angles more than 180° represent vertebral extension (i.e., with hips above the chest plane). We considered the tail angle on the frontal plane as the clockwise rotation of the tail about the mid-hip point, whereby an angle of 180° represents the directly posterior position and angles < 180° and > 180° represent right and left displacement, respectively. To calculate the angle of the tail in the sagittal plane, we determined the angle between the tail and the abdominal plane, whereby negative and positive angles are below and above the abdominal plane, respectively.
To estimate shoulder adduction angle, we calculated the angle between the humerus and the chest plane. Negative and positive angles represent ventral and dorsal positions, respectively. We computed the shoulder retraction as the angle between the line connecting the shoulders and the humerus, as projected onto the chest plane. A positive shoulder retraction angle represents an anterior position, and a negative angle represents a posterior position. Similarly, we used the left hip, the right hip, and the midbody to define an abdominal plane which we coordinated with the knee and the hip axes to calculate hip adduction and retraction. To estimate elbow flexion angles, we calculated the angle between the humerus and the vector defined by the elbow and the wrist for the right and left forelimbs. Similarly, we estimated knee flexion angles using the femur and the vector defined by the knee and ankle. Since shoulder and elbow actuation can both change hand position, we also quantified the position of the wrist relative to the shoulder using the same method as with shoulder retraction, but using the wrist instead of the elbow.
For velocity estimates, we used the x-, y-, and z-components of the midbody because it was the landmark nearest to the center of body mass (i.e., approximately midway between the forelimbs and hindlimbs; see [33]). Vertical speed was taken to be the z-component of the body velocity. To calculate horizontal speed, we used the square root of the sum of the squares of the x- and y-components, which we then used to calculate forward speed, defined here as the horizontal speed in the direction of the gecko’s body axis such that a positive forward speed was in the gecko’s cranial direction. We estimated glide angle as the angle between the vertical speed and the horizontal speed, and considered forward speed, vertical speed, and glide angle to be the primary measures of glide performance. Similarly, we used the x-, y-, and z-components of midbody acceleration to calculate analogous acceleration values.
Since the kinematic focus of this study was on the novel behavior whereby geckos carried out symmetric and large-amplitude limb motions, we used Pearson correlation tests to detect bilaterally symmetric patterns in limb movements to decide which trials should be used for more detailed analyses, and to characterize gliding postures, detailed below as “skydiving” and “swept configuration” (see Fig. 1; Table 1). A limb motion was considered bilaterally symmetrical if the left and right sides showed a significant and positive correlation for that motion (e.g., shoulder retraction angle). Although we observed that the forelimb motions were far more dramatic than those of the hindlimbs, we nonetheless tested for bilateral symmetry between the left and right hip angles in each of the 32 trials. Whereas many of the trials demonstrated correlations between the left and right side for both the retraction and adduction angles, the signs of those movements were inconsistent and resulted in similar proportions of symmetrical and asymmetrical movements (Table 2). Since we were specifically interested in characterizing symmetric limb movements in this study, we did not further detail hindlimb kinematics.
For the forelimbs, the change in shoulder retraction angle was noticeably more dramatic than that of the shoulder adduction angle (Movie S1-S2; Table 1). Thus, we only tested for bilateral symmetry between the left and right shoulder retraction angles (Table 2), opting to instead include shoulder adduction angle as a dependent variable in downstream analyses. Shoulder retraction angles were significantly and positively correlated (p > 0.004 in all cases), with correspondingly symmetrical movements, in only 24 of 32 trials. Of these, we used only those trials in which at least one of the shoulders moved through a retraction angle of at least 45º, thus eliminating all trials in which the shoulder retraction angles were either small or not positively correlated (i.e., not symmetrical) for detailed characterization of the forelimb retraction behavior. 20 trials were thus identified further kinematic analysis (Table 2). We used limbs from only one side per trial for analysis, selecting the side for which the forelimb was more representative of the retraction behavior (e.g., moved through a greater angle). In total, we analyzed 12 right forelimb sweeps and 8 left forelimb sweeps (Table 3).
We considered the starting point for forelimb sweep to be either the local minimum immediately prior to large-amplitude shoulder retraction (Figure S1) or the first recorded frame. The end of forelimb sweep was defined as the first local maximum following shoulder retraction. We calculated the sweep amplitude as the difference between retraction angles at the start and at the end of this behavior. The half-period of the behavior was calculated as the difference in time between its start and end.
Qualitative observation suggested that large-amplitude shoulder retraction enabled transition between two primary gliding postures that we thereafter referred to as “skydiving” and “swept configuration”. The skydiving posture is comparable to that previously described in the literature [19, 21], with the body and tail flattened and the limbs laterally extended. Here, we distinguish the skydiving posture from the swept configuration primarily by the position of the forelimbs. Qualitatively, the forelimbs are held in a protracted (anterior) position in the sky-diving posture, whereas in the swept configuration, the forelimbs are retracted posteriorly. We used the single frame in which the local minimum marking the start of the shoulder sweep for the skydiving posture, and the local maximum for the swept configuration (Figure S1). At each of these timepoints, we characterized overall posture of the geckos by determining mean values of the pose variables among the trials (Table 1; Fig. 2). We then compared mean values for each of these variables using paired t-tests between skydiving and the swept configuration if the variable was normally distributed (determined by Lilliefors test; Table S1), or with a Wilcoxon signed-rank test if not (Table 1).
Behavioral variation in forelimb retraction was evident among trials (see Table 3). In nine of 20 trials, geckos performed a recovery stroke before the end of data collection, whereby the limbs exhibited substantial protraction towards the skydiving position (assessed visually, e.g., Figures S2-S3; Table 4). In all of these trials, the limbs returned to within 36% of each particular trial’s sweep amplitude relative to the initial retraction angle, as measured at a local minimum after the sweep or at the last frame of data. From these data, we determined the sweep duration. We characterized the posture at the end of the recovery stroke with the same measurements as were used for the skydiving and swept configuration postures. For these nine trials, we performed a repeated-measures ANOVA and Tukey–Kramer posthoc-analyses to compare the skydiving posture, swept posture, and “recovery” postures if the variable was normally distributed (determined by Lilliefors test; Table S1), or with a Friedman test and Dunn’s posthoc analyses if not (Table 4).
Occasionally, the gliding event was long enough to capture multiple shoulder retractions. Only two trials (gecko 3: trial 9 and Gecko 5: trial 10B) included two clear local maxima (e.g., Figure S1) indicative of the end of limb retraction. In addition to these trials, we analyzed three trials that captured a full sweep and also a sweep segment wherein the local maximum occurred prior to the onset of data collection, but for which the included data were greater than the local maximum used to define the sweep (e.g., Figure S2). Whereas this approach does not capture the entire behavior (and thus overestimates the effective reciprocation frequency), it does demonstrate the frequency at which the gecko could potentially sweep its arm from the maximum value to the minimum and then back to the maximum. The inverse of this period of sweeping motion was assumed to indicate such a potential reciprocation frequency.
In some trials, the gecko did not return their forelimbs to the skydiving position after the sweep either immediately (or at all), but instead oscillated the shoulders with low-amplitude changes in retraction angle (see Figure S1). We defined occurrence of this behavior as when trials showed a local minimum in shoulder retraction after the sweep, but either before a return to the skydiving position or with no such return. This behavior occurred for at least one cycle in 14 of 20 trials. As with forelimb sweeps, we recorded the amplitude and half period of these displacements, but we used the local maximum and minimum positions to indicate the start and end of the behavior, respectively.
We performed Pearson correlation analyses between shoulder retraction angle and adduction angle. We also performed cross-correlation tests [34] in RStudio [35, 36] to identify relationships between the postural variables of shoulder retraction angle, pitch, and body bend (Table S2). For each of these postural variables, we performed cross-correlations with tail angle in the sagittal plane, forward velocity, vertical velocity, forward acceleration, and vertical acceleration (Tables S1-S2), so as to identify relationships between them while taking into account potential time lag between series (see [10]). Briefly, cross-correlation tests evaluate relationships between potentially lagged time series by staggering data incrementally across a variable number of time steps, and then generating correlation coefficients for each time step and corresponding lag (negative and positive lag values indicate that series 2 is shifted backwards or forwards in time, respectively, relative to series 1). However, because the relationships of interest were those of limb posture on glide performance, we only identified the positive lag for the closest and highest coefficient relative to zero lag. Finally, we performed a Pearson correlation test on the lagged dataset (see Fig. S4).
Japan is on the verge of a boom in agricultural production, according to Takeshi Sudo, project director at agribusiness consultant Agri Connect. Much like the Japanese economy in the sixties of the last century, Sudo believes the heyday of Japan’s agriculture is yet to come.
Sudo spoke at a seminar at the Farm Design & Development Forum, held at J Agri Week last October, where he laid down the recipe for successful agribusiness development: implementing cross-industry strategies and collaboration between local authorities. Sudo knows what he is talking about, having been formerly responsible for new business development at Fujitsu (2012-2022), in which role he helped establish new greenhouse businesses like Smart Agriculture Iwata (SAC Iwata) and Grand Bouquet Otaki (GBO).
Both are successful cases of collaboration between local authorities and external parties (investors) resulting in appealing business models. SAC Iwata is a 8,5 ha cluster of mid-to-high tech greenhouses, cultivating complementary crops, with shared research and shipping facilities. GBO is a cutting edge orchid cultivation facility invested in innovation in packaging and shipping. Sudo managed both business entities on behalf of Fujitsu before they were sold on to the next investors. SAC Iwata is now part of Daiwa Food & Agriculture, and GBO of Aucnet Group.
Clearly, such development is not yet happening all over Japan. When considering the promotion of new agribusiness in their areas, most local authorities tend to get hung up on the present situation, focusing on current farmers and crops, unable to look beyond the farming perspective. But Sudo is convinced local authorities eventually will sign up for a broader business development perspective in which farming is considered in conjunction with other activities. With over 120 agribusiness projects by local authorities currently in development, Sudo has reason to be optimistic. And he is ready to do his part for more.
At the seminar, Sudo lectured on the need for ‘design thinking’, calling on local officials to approach business development from the perspective of desirable business models and to seek out companies and organizations able to bring about the desired results for collaboration on an equal footing. He also pointed at the need for close collaboration between various levels of local authorities, given the divided responsibilities for budget and planning, as well as collaboration beyond local borders at the level of business operators linking production strengths from various regions.
In the Netherlands, we know all about successful agribusiness projects and clustering, as Sudo is well aware. In his presentation, Sudo referred to Agriport A7 as a good illustration of what is possible if companies’ needs take center stage in local business development. It would be great if our companies and knowledge institutes could offer support to this development in Japan as well, not merely by serving as a model, but by becoming active partners in collaborative projects.
Copenhagen, Denmark – A.P. Moller – Maersk A/S (OMX: MAERSK-B) delivered strong financial results in the third quarter of 2025, driven by operational improvements and proactive cost measures. The company achieved sequential growth across all business segments. Based on this, Maersk refines the full-year 2025 financial guidance.
We have delivered a strong third quarter across our business. Our performance reflects our ability to execute and continuously improve, as well as the trust customers place in us. The new East-West network has strengthened our Ocean performance, delivering industry-leading reliability, higher volumes and lower costs. Terminals achieved another record quarter with strong volume growth, and Logistics & Services continued to enhance profitability. As market conditions fluctuate, we are well positioned to help our customers adapt and maintain stability across their supply chains.
Maersk refines the full-year 2025 financial guidance by raising the lower end as per the table below. The expected global container market volume growth has also been revised to be around 4% (previously between 2% and 4%). The Red Sea disruption is expected to last for the full year.
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Guidance 2025 |
EBITDA Underlying (6 February: 6.0-9.0) (7 August: 8.0-9.5) |
EBIT Underlying (6 February: 0.0-3.0) (7 August: 2.0-3.5) |
Free cash flow or higher (6 February: -3.0 or higher) (7 August: -1.0 or higher) |
CAPEX 2024-2025 (6 February: 10.0-11.0) (7 August: Unchanged) |
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Financial performance for Maersk for 2025 depends on several factors subject to uncertainties related to the given uncertain macroeconomic conditions, bunker fuel prices and freight rates. All else being equal, the sensitivities for 2025 for four key assumptions are listed below:
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Full Q3 2025 financial report available here.
A.P. Moller – Maersk is an integrated logistics company working to connect and simplify its customers’ supply chains. As a global leader in logistics services, the company operates in more than 130 countries and employs around 100,000 people. Maersk is aiming to reach net zero GHG emissions by 2040 across the entire business with new technologies, new vessels, and reduced GHG emissions fuels*.
*Maersk defines “reduced GHG emissions fuels” as fuels with at least 65% reductions in GHG emissions on a lifecycle basis compared to fossil of 94 g CO2e/MJ.
Marjan Rintel, CEO of KLM, commented: “Once again, we’ve carried more passengers, and our revenue has increased slightly. However, high costs and operational challenges continue to trouble us. Our ‘Back on Track’ programme is delivering results, but we still have a long way to go. We need to steer decisively and make clear choices to get KLM structurally back on track and ensure we can continue investing in our future.”
Third Quarter: Costs Weigh on Results
The KLM Group’s operating profit fell by 14% in the third quarter compared to the previous year. This decline is mainly attributable to rising costs, including higher airport charges and labour costs, as well as operational disruptions. The impact of strikes at KLM ground handling and cargo also weighed on the results.
First Nine Months: Savings Insufficient to Offset Costs
Over the first nine months of 2025, KLM Group’s operating profit amounted to €339 million, a decrease of €26 million compared to the same period in 2024. Revenue rose by 4% to €9.9 billion, primarily due to a 4.4% increase in capacity. KLM’s ‘Back on Track’ programme contributed more than €300 million in savings and additional revenue during this period and is therefore on target. Furthermore, an agreement in principle was reached with the Dutch Airline Pilots Association (VNV), which will make more capacity available. However, all these measures remain insufficient to offset the persistently high costs.
Bas Brouns, CFO KLM, added: “The improvement programme is working, with initiatives having been taken throughout the company. Among other things, we have successfully renegotiated contracts, been able to operate more flights and are using digital solutions more effectively. Without these improvements, our financial position would be significantly weaker. Nevertheless, we are still not earning enough to make the necessary investments in customers, people and operations to keep KLM competitive and future-proof. That is why we continue to actively seek additional improvements to strengthen our financial base.”
Business Units: Mixed Picture
Transavia saw its operating profit decline by €29 million in the first nine months, attributed to increasing competition and rising costs. Conversely, the Engineering & Maintenance division made a positive contribution of €41 million. Cargo managed a slight improvement in its results year-on-year, despite operational challenges and the introduction of a new handling system. This underscores the importance of a continued focus on profitable growth and operational stability across all parts of the KLM Group.n een voortdurende focus op winstgevende groei en operationele stabiliteit binnen alle onderdelen van de KLM Groep.
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Tariffs and import-tax shifts reshape global tradeflow as US imports fall and China redirects exports to Asia and Europe.
Following Donald Trump’s visit to Asia in late October, Washington and Beijing struck a deal that cuts the US fentanyl-related tariff share to 10% from 20%, suspends the expanded sanctions list, extends Section 301 exclusions, and maintains the pause on 24ppts of reciprocal tariffs. On China’s side, rare earth restrictions will be delayed and soybean purchases resumed. Both parties also agreed to halt shipping levies that took effect in October. The reduction in tariff and non-tariff barriers supports an upward revision to our China GDP forecast for 2026 to 4.3% from 4.0%.
The surge in US imports earlier this year, as firms front-loaded ahead of tariffs, is now unwinding. Goods imports fell 7% m/m in August and are almost 10% below the pre-tariff trend. Tariffs will continue to hinder US imports, resulting in a 6.4% decline through 2026.
◼ After a strong first half in 2025, global trade volumes are set to decline 0.3% in 2026, with nominal trade growth slowing to 1.6% from 4.4% this year. Despite lower tariffs, China’s exports to the US are set to drop 18% by 2026, reinforcing the structural pivot of Chinese trade away from the US and toward ASEAN and Europe.
◼ Containerised trade is set to grow more than 2% annually as production relocates across Asia and regional supply chains deepen. In contrast, dry and liquid bulk shipments face softer demand, as weaker industrial activity reduces flows of oil, coal, and metals. This will be partly offset by rising LNG and precious metal exports tied to the AI and clean energy booms.
◼ Our latest trade forecasts were released on October 29. These can be accessed through the TradePrism Dashboard or Snowflake Marketplace.
This report was brought to you by the TRADEPRISM team.
Economic insights, modelling, and development advice for the Australian transportation sector.
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Plans have been lodged for an AI factory in Derbyshire that would provide “critical” computing infrastructure.
Carbon3.AI has submitted a planning application to Chesterfield Borough Council to build a modular facility on land next to the M1 at Erin Road landfill between Duckmanton and Poolsbrook.
According to planning documents, the site was chosen next to the Valencia Energy Centre – which uses gas from landfill to produce renewable energy – as it would provide electricity to the AI factory using private wire connections.
Plans said the proposal would address an “urgent need” for sovereign AI capacity.
Documents added the modular nature of the proposed facility would make the development “easy” to upgrade as well as remove if operations stopped or were relocated.
Planning documents said: “The proposed facility is of strategic regional and national importance.
“It will deliver critical sovereign AI computing infrastructure for the UK, addressing the pressing national requirement for secure, sustainable, and accessible high-capacity digital computing resources.
“The development will support local and regional economic growth, attract investment, create highly skilled jobs, and demonstrate the integration of renewable energy into advanced technological infrastructure,” plans added.
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China has issued dollar bonds at rates equivalent to US Treasury yields, in what bankers on the deal said was the first time Beijing’s borrowing costs have matched Washington’s.
The bond offering is the latest example of countries taking advantage of being able to issue international debt cheaply, as their borrowing costs in relation to US Treasuries fall to some of the lowest levels on record.
China’s issuance follows a de-escalation in trade tensions between Beijing and Washington after US President Donald Trump and Chinese President Xi Jinping agreed to a one-year trade truce last week.
China’s Ministry of Finance issued $4bn of dollar bonds in Hong Kong, pricing the three-year bond on par with US Treasury equivalents, with a coupon of 3.625 per cent. The five-year bond was priced 0.02 percentage points above equivalent US Treasuries.
“Markets are flush with liquidity and geopolitical tensions have eased,” said David Yim, head of capital markets, Greater China and North Asia, at Standard Chartered, which was one of the bookrunners for the deal.
Another person noted that investors were interested in Chinese sovereign dollar debt to diversify their portfolios and that the relatively limited issuance had led to the oversubscription.
Order books showed that the five-year bond was 30 times subscribed, with more than half of the offers coming from central banks, sovereign wealth funds and insurance companies.
In September, Abu Dhabi sold $2bn in 10-year bonds at a spread to US Treasuries of 0.18 percentage points. South Korea’s finance ministry issued $1bn of dollar bonds with a maturity of five years at a spread of 0.17 percentage points in October.
China last issued dollar bonds in 2024, when it sold $2bn of debt in Saudi Arabia.
A syndicate of Chinese, US and other foreign banks managed Thursday’s deal. The proceeds will be used for “general government purposes”, according to the bond term sheet. The deal will be settled next Thursday.
While Chinese dollar-denominated debt has previously traded at a negative spread to US equivalents, bonds have always been priced at a premium, although the spread has narrowed.
[Beijing, China, November 5, 2025] Leading independent network security evaluation organization AV-TEST, based in Germany, has released its latest results for the Advanced Threat Protection (ATP) test. Huawei’s HiSec Endpoint intelligent endpoint security system achieved a perfect score across all 10 core test items, earning certification with outstanding performance. This result not only confirms HiSec Endpoint’s technological leadership in advanced threat detection, but also provides a trusted endpoint security solution for critical scenarios such as enterprise environments.
The AV-TEST ATP evaluation centers on simulating real-world attack scenarios, focusing on stealthy and highly evasive malware. The 10 test items span the entire attack lifecycle, each with stringent performance and effectiveness thresholds. Huawei HiSec Endpoint passed with full marks thanks to four core technical strengths that enable precise detection and prevention of hidden threats:
• Dual-engine collaborative detection. HiSec Endpoint adopts a hybrid architecture combining static virus detection and dynamic analysis. The static engine, powered by Huawei’s proprietary next-generation Content-based Detection Engine (CDE), deeply verifies file integrity. The dynamic engine leverages the exclusive Graph Streaming Process Engine (GSP) to monitor memory loading situation in real time, capturing hidden malicious module injection. The synergy between the two engines enables precise blocking of malicious programs.
• Full-link memory tracing. HiSec Endpoint builds a comprehensive capability combining full-stack data recording and dual-mode memory inspection. On one hand, kernel-level probes track process creation and code injection to pinpoint abnormal memory writes. On the other, an innovative endpoint-cloud collaborative memory inspection system uses AI clustering for memory fingerprinting on endpoints, while the server aggregates global samples for continuous AI training and model optimization. This evolving memory threat awareness effectively exposes even fileless execution situations.
• Intent analysis. The built-in high-performance GSP engine constructs a “shadow graph” within protected memory, covering standard operations such as processes, files, registry access, and HTTPS communication. It also accurately detects covert situations like memory access, thread hijacking, and process injection.
• In-depth threat handling. Upon threat detection, the GSP engine generates a real-time threat propagation subgraph and executes deep remediation along the graph. Beyond basic actions like terminating malicious processes and isolating suspicious files, it supports fine-grained operations such as restoring critical registry keys, deleting malicious scheduled tasks, and uninstalling persistent services, thoroughly eliminating traces of long-term malware presence while maintaining system stability.
Huawei’s perfect score in the AV-TEST ATP evaluation is a direct testament to the technical strength of HiSec Endpoint. Looking ahead, as AI-driven attack techniques continue to evolve, Huawei will keep advancing its security capabilities, making every endpoint a fortress of security in the digital world.
“Attestor recently initiated a process to identify strategic options for Condor,” Gerber told Reuters.
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Attestor declined to comment.
In 2021, Attestor bought 51% of Condor from the German government, which had bailed out the airline after the collapse of former parent Thomas Cook and provided further financial support during the pandemic.
Condor plans to repay the granted state aid to state-backed lender KfW (KFW.UL) by the end of 2026, by which point Attestor could take over the government’s remaining stake at a previously agreed price.
“It is important for Condor that it can continue to invest as much as possible and expand its reach,” said Gerber.
Bloomberg last week reported that Attestor was seeking a partner for its aviation business, which includes Condor and Marabu Airlines, and that it was working with Barclays, citing people familiar with the matter.
Reporting by Ilona Wissenbach; Writing by Christoph Steitz; Editing by Rod Nickel
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