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Landslides and collapses seriously threaten the safety of residents in Xiangtan County, Hunan Province, southern China. Local human engineering activities, dominated by slope cutting for housing construction, create steep artificial free faces, greatly weakening slope stability and becoming the key anthropogenic driver aggravating geological hazard risks, while targeted quantitative susceptibility assessment for residential slope units is still lacking for precise disaster prevention. To fill this gap and support proactive geohazard mitigation, this study selects Xiangtan County as the research object. A total of 166 landslide and collapse hazard points and 869 moderately and highly susceptible residential slope units were collected, and 12 conditioning factors, such as relative height difference, average slope and engineering rock mass group, were subsequently screened. Three hybrid intelligence models, namely PSO-BP, PSO-RF and PSO-SVM, were established to map residential slope unit susceptibility across the whole study area. The ROC-AUC and Kappa coefficient were adopted to quantify and compare the predictive performance of each model, and the Jenks natural breakpoint method combined with field survey data was used to classify all 7257 residential slope units into three susceptibility grades. The evaluation results show that the PSO-RF model performs best with an AUC of 0.913 and a Kappa coefficient of 0.64, representing strong predictive reliability. Under this optimal model, moderate-susceptibility units account for 12.94% (939 units) and high-susceptibility units account for 0.69% (50 units), both of which are concentrated in the southwest, southeast and partially northern zones of the county where intensive human slope-cutting activities prevail. This research provides a feasible technical framework for identifying high-risk residential slopes and delivers clear data support for local geohazard risk control and disaster reduction. In summary, the PSO-RF hybrid model is proven suitable for fine-scale susceptibility assessment of residential slopes in hilly regions with frequent small-sized slope failures. Full article

Ploughing constitutes a fundamental tillage practice for enhancing soil fertility as well as water and fertilizer utilization efficiency. The uniformity of tillage depth directly determines seedling emergence consistency and final crop yield. Conventional indirect tillage depth detection approaches based solely on the rotation angle of the tractor lift arm are susceptible to disturbances induced by uneven terrain and dynamic attitude fluctuations of the tractor-implement system. To compensate for disturbances induced by tractor pitch angle variations, existing pitch compensation models adopt cascaded output compensation to calculate tillage depth. Nevertheless, these models need recalibration after each modification to the three-point hitch linkage length and are incapable of simultaneously compensating for pitch variations in the tractor and attached implement. To reduce the sensitivity of the tillage depth model to structural changes in the three-point hitch, this work constructs a mapping between hitch structural geometric deviations and vertical projection offsets of key linkages via tractor–plough integrated distributed attitude sensing. In this paper, a distributed tillage depth model is developed, which takes the distributed attitude of the whole working unit and the pitch of the lower-link measured by a rotation angle sensor as independent input variables. Field validation was performed with a mounted moldboard plough, with ultrasonic ranging sensor tillage depth readings serving as the reference benchmark to assess the reliability and precision of the proposed distributed model. Experimental results show that compared with the pitch angle cascaded tillage depth model, the average root mean square error of the proposed method is reduced from 22.5 mm to 12.5 mm on bumpy fields and from 11.3 mm to 8.7 mm on flat fields. The distributed model can significantly improve the measurement accuracy and anti-interference capability of tillage depth detection on uneven farmland, and it is applicable to adaptive measurement and control of tillage depth in complex ploughing scenarios. Full article

Objectives: Preterm infants confront multiple coexistent health risks and may not receive human milk (HM), which contains human milk oligosaccharides (HMOs), among other biologically active, beneficial components. 2′-Fucosyllactose (2′-FL) is the most abundant HMO in most HM. Interventional studies of formula with added HMOs have demonstrated multiple beneficial outcomes in healthy, term infants. Methods: This double-blind, randomized, placebo-controlled pilot study (NCT03306316) evaluated the effect of a 2′-FL supplement on growth, tolerance, and neurobehavior, as assessed by the Neonatal Intensive Care Unit (NICU) Network Neurobehavioral Scale (NNNS), in preterm infants. Eligible neonates born between 26 0/7 and 31 6/7 weeks gestational age (GA) were enrolled from three NICUs in the United States. Preterm infants consuming mother’s own milk or donor human milk were randomized to receive a supplement of 0.135 g/mL of 2′-FL or 0.05 g/mL dextrose (placebo) twice daily until 36 weeks corrected GA, NNNS assessment, or hospital discharge, whichever came first. Results: Of 42 participating preterm infants, 23 (55%) received 2′-FL and demonstrated no significant differences in growth in weight, length, or head circumference over 5 weeks. There were also no differences in stool frequency, adverse (AEs) or serious adverse events (SAEs), or neurobehavior (NNNS) between groups. Conclusions: This pilot study found that 2′-FL supplementation in preterm infants through 36 weeks corrected GA was safe and well-tolerated and supported adequate growth and neurobehavior. Full article

Decarbonization has been set as the ultimate target of the European energy policy, and, under this frame, Greece has substantially reformed its electricity mix over the last two decades. Yet, the rapid transition of the country’s electricity production system towards a model that strongly relies on non-controllable renewables, without improving its energy storage means, has caused several adverse impacts that reasonably raise serious concerns about its sustainability. Further questions arise when accounting for the projections and underlying assumptions reported in the National Energy and Climate Plan. In this vein, key policy, societal, technical, sustainability and science issues are discussed, involving the challenges, perspectives and potential drawbacks of Greece’s long-term energy planning . To reveal the critical role of storage, a toy simulation-optimization model is applied at the national scale to evaluate several scenarios of wind and solar P/V development under the premise of a theoretically fully decarbonized mix. This allows the exploration of trade-offs between energy storage and power capacity needs against key performance metrics (frequency and quantity of deficits and percentage of rejected renewable energy). Preliminary feasibility issues are also highlighted by considering alternative configurations of pumped hydropower storage units and associating their approximative investment costs with scale and geometry. Full article

Industrial chili peppers are specialized varieties primarily used for the extraction of capsaicinoids and paprika red. Their post-harvest processing level directly affects product quality and industrial economic benefits. Most existing studies have focused on a single unit operation or on edible chili peppers, and a systematic review of the entire post-harvest processing chain for industrial chili peppers is still lacking. Taking the standardized post-harvest processing workflow of industrial chili peppers as its core theme, this paper systematically reviews the current research approaches and application status of industrial chili pepper post-harvest processing technologies across six core unit operations, namely cleaning and impurity removal, grading and sorting, drying, stem and seed removal, crushing and grinding, and extraction of bioactive compounds. The analysis indicates that the field currently faces four common challenges: the lack of standardized processing parameters for classified processing, relatively low drying energy efficiency, insufficient online sensing and intelligent collaborative control, and a scarcity of industrial-scale validation for emerging technologies. This paper further constructs a technical route and technology evaluation framework for the entire post-harvest processing chain of industrial chili peppers, clarifies the applicable boundaries and scale suitability of different processing technologies, and provides a theoretical basis for industrial technological upgrading and process selection. Full article

Wearable inertial measurement units (IMUs) offer an objective, low-cost basis for Parkinson’s disease (PD) assessment, but multi-task clinical protocols yield heterogeneous recordings across body locations and small cohorts, and it is unclear whether such data can support reliable PD detection without training deep models from scratch. We therefore ask whether a motion-pretrained representation transfers to this setting, and quantify how much of the discriminative signal it supplies. Each subject is represented by five task-level motion embeddings, one per clinical task, produced by a frozen pretrained spatio-temporal graph convolutional network (ST-GCN) that fuses the thirteen body-worn sensors into a whole-body embedding; a three-layer Transformer with validity-mask weighting aggregates these tokens for binary PD-versus-control classification on the WearGait-PD cohort (181 subjects: 100 PD, 81 controls). Under a leakage-free nested protocol with repeated subject-disjoint stratified 5-fold cross-validation (5 seeds; 25 estimates per model) and paired significance testing, the model attains a balanced accuracy of 0.834 ± 0.087, macro-F1 of 0.842 ± 0.094, and AUC of 0.842 ± 0.103. It leads six classical baselines and a spectrogram-CNN on accuracy-based metrics, though random forest, gradient boosting, and the spectrogram-CNN edge ahead on AUC; after correction for fold correlation, none of these between-model differences is significant. The one robust finding is a transfer effect: replacing the pretrained encoder with a random one of identical architecture lowers balanced accuracy by 15.5 points when frozen (p = 0.043) and 20.4 when trained end-to-end (p = 0.014). Discrimination is preserved under 1:1 age matching (0.846) and across both genders, so it is not explained by age imbalance. Motion-pretrained skeletal encoders thus supply the majority of the discriminative signal, while the aggregator contributes gains inseparable from noise at this cohort size. Full article

Dixie Valley, located in west-central Nevada within the Basin and Range Province, is one of the most important geothermal systems in the western United States and an increasingly attractive target for critical-mineral exploration. The valley combines active extensional tectonics, major range-front and intrabasin fault systems, high heat flow, hydrothermal alteration, and thick sedimentary basins that may provide favorable conditions for the development of geothermal reservoirs and lithium-bearing brines or clays. This paper presents an integrated multiphysics interpretation of gravity, magnetic, helicopter-borne time-domain electromagnetic (HeliTEM), and magnetotelluric (MT) data from Dixie Valley, with emphasis on the Grover Point area investigated by the Basin and Range Investigation for Developing Geothermal Energy (BRIDGE) program. We apply joint Gramian inversion of gravity and magnetic data to recover mutually consistent density and magnetization models, including separate induced and remanent magnetization components. We also perform rigorous 3D inversion of HeliTEM data and cooperative 3D inversion of HeliTEM and MT data to obtain a resistivity model extending from the shallow basin fill to deeper fault-controlled geothermal structures. The integrated interpretation identifies low-density sedimentary basins, induced magnetization highs related to magnetic basement or intrusive rocks, remanent magnetization variations associated with basement architecture and hydrothermal alteration, and conductive corridors interpreted as clay-rich alteration zones and possible hydrothermal pathways. These results demonstrate that integrated gravity, magnetic, HeliTEM, and MT inversion can substantially reduce interpretation ambiguity and improve targeting of concealed geothermal systems and associated lithium resources in extensional terranes. Full article

Indoor temperature forecasting in large public buildings is affected by heating, ventilation, and air-conditioning (HVAC) control, occupancy, airflow, and zone use, not only by recent temperature histories. This study asks whether operationally derived functional-zone proxy labels—room groupings derived heuristically from HVAC statistics rather than from field-verified building metadata—can provide a useful learnable context for temporal convolutional network (TCN)-based room-level forecasting when verified semantic metadata are unavailable. Using the public BEAR (Building Efficiency and Renewables) multizone building HVAC dataset, we construct a reproducible operational proxy scenario: eighty rooms are grouped from occupancy, airflow, damper, control-command, and temperature-variability statistics, and Niagara-inspired point-naming conventions are used only as structured metadata descriptors, not as tags exported from a deployed system. The proposed SemanticTCN augments a TCN backbone with learnable room, temperature-variability proxy embeddings. Experiments compare persistence, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), TCN, DLinear, PatchTST, a TCNOnly variant, SemanticTCN, and ablations for 1 h, 6 h, and 24 h forecasts, evaluated by mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination ($R^2$). Persistence is strongest at 1 h and 24 h, while DLinear is strongest at 6 h, indicating strong thermal inertia and daily regularity. Within the TCN family, SemanticTCN improves over TCNOnly in the locked run, shows a statistically significant within-distribution MAE improvement across multiple seeds (most consistently at 6 h), and reduces large-error rates; however, a leave-one-zone-out experiment shows that this advantage does not transfer to rooms unseen during training, and a five-seed operational-versus-random-proxy control shows that the specific operational grouping rule is not statistically distinguishable from arbitrary grouping. The bounded conclusion is that providing room/group context to the TCN backbone helps predict rooms whose identity was observed during training, especially at 6 h, but the gain reflects room-identity context rather than generalizable operational semantics: the proxy labels are heuristic operational constructs rather than verified building semantics; they do not outperform arbitrary grouping under multi-seed control, and they do not generalize to unseen rooms under this split. Full article

This work presents the design, development, and experimental validation of an inductive sensing system for monitoring prosthetic socket fit variations caused by residual limb volume fluctuations. The system aims to reduce the risk of discomfort and tissue injury by measuring the distance between the outer rigid socket and the inner silicone elastomeric liner worn in direct contact with the residual limb. The sensing architecture consists of a portable data acquisition unit, an LC resonator sensor mounted on the inner surface of the rigid socket, and a magnetic silicone target attached to the external surface of the liner. Multiple configurations of LC resonators and magnetic targets were designed and evaluated. The results indicate that a medium-sized coil (outer diameter = 28 mm, capacitance = 181 pF) combined with a 1 mm thick silicone target made of Ecoflex™ 00-50 with 70 wt% NdFeB microparticles provides the most stable and sensitive performance. Experiments demonstrated stable distance detection up to 7 mm, with the resonant-frequency shift (relative to the baseline condition) varying from −31.37 kHz at 0 mm to −2.94 kHz at 7.00 mm, for a total shift range of 29.84 kHz. Environmental tests showed minimal drift, with frequency variations below 0.40 kHz across temperature (25–60 °C) and humidity (50–90% RH) changes. In vitro validation using a high-fidelity residual limb simulator and an adjustable socket reproduced controlled residual limb volume variations of 300 mL (i.e., +7.5%), resulting in repeatable resonant-frequency changes within 3.15–3.17 MHz with measurement variability ($u_A$, Type A) below 0.13 kHz. Full article

Background: Accurate thyroid volume measurement is essential for diagnosing and monitoring thyroid disorders and for determining radioiodine therapy dosage. The ellipsoid method applied to two-dimensional B-mode ultrasound (Bmode-2D-US) is the most prevalent clinical approach, yet it is known to suffer from high observer dependence and limited accuracy. Tracked three-dimensional ultrasound (3D-US) is a promising radiation-free alternative, but direct comparisons across modalities under controlled conditions remain scarce. Methods: A custom-built anthropomorphic multi-modality phantom was constructed containing six anatomically realistic thyroid lobe samples molded from segmented MRI data, with ground truth volumes verified by the suspension method and 3D surface scanning. Volume measurements were performed by six observers of varying experience using Bmode-2D-US, electromagnetically tracked 3D-US (EM-3D-US), inertial-measurement-unit-tracked 3D-US (IMU-3D-US), and optically tracked 3D-US (Opt-3D-US), as well as computed tomography (CT) and magnetic resonance imaging (MRI). Inter- and intraobserver variability were assessed using modified Bland–Altman analysis. Results: All three 3D-US methods reduced inter- and intraobserver variability compared to Bmode-2D-US, achieving variability comparable to CT and lower than MRI. Mean accuracy was similar across 3D-US, CT, and MRI. Bmode-2D-US showed strong observer dependence, with operator experience having the most pronounced effect on this method. Among tracked methods, EM-3D-US and Opt-3D-US were least sensitive to operator movement quality, while IMU-3D-US showed somewhat higher sensitivity. Conclusions: Tracked 3D-US is a promising radiation-free alternative to conventional Bmode-2D-US for thyroid volumetry, offering improved reproducibility and accuracy across operators of varying experience in this phantom-based evaluation. Full article

Diesel generator sets are key frequency-supporting units in islanded microgrids and shipboard power systems, where rapid speed recovery under abrupt load variations is essential for maintaining power quality. However, conventional linear active disturbance rejection control (LADRC) is limited by the disturbance-estimation and noise-amplification trade-off of a single observer, while fixed parameters restrict its adaptability under varying operating conditions. To address these limitations, this paper proposes an RBF neural-network-optimized cascaded LADRC method, termed RBF-CLADRC. A mechanism-based torque balance model is first established, with uncertain mechanical coupling, friction losses, and load variations lumped into the total disturbance. A residual-disturbance cascaded observer is then constructed, in which the first linear extended state observer estimates the total disturbance and the second further reconstructs the residual estimation error. Unlike conventional ML-based ADRC methods that directly tune multiple gains, the proposed RBFNN adjusts only a common controller bandwidth within a prescribed interval, while all observer and feedback gains are generated through predefined analytical relationships. This low-dimensional adaptation preserves coordinated gain variation, reduces online computational complexity, and facilitates real-time implementation. Lyapunov analysis shows that the observer and tracking errors are uniformly ultimately bounded under bounded disturbance rates and converge exponentially for constant disturbances. Finally, comparative simulations in MATLAB/Simulink demonstrate that the proposed method achieves better dynamic response and disturbance-rejection performance than conventional LADRC and other benchmark controllers. Full article

Accurate multi-horizon forecasting of greenhouse air temperature is essential for sensor-based environmental monitoring and control-oriented decision support. However, conventional evaluation methods often rely on open-loop or semi-closed-loop settings, where future observations may be unavailable during practical deployment. This study established a strict recursive closed-loop forecasting protocol and an exogenous-error propagation analysis framework using multi-source environmental sensing data. A Liquid Neural Network (LNN) was developed as a continuous-time forecasting model and compared with long short-term memory (LSTM), gated recurrent unit (GRU), and extreme gradient boosting (XGBoost) under consistent data conditions. During inference, future greenhouse air temperature and exogenous variables were not observed but recursively generated through predicted-temperature feedback and exogenous-variable forecasting. Across 10 repeated runs, the LNN achieved the lowest one-step prediction error, with a mean absolute error of 0.4771 °C and root mean square error of 0.5996 °C. However, under strict recursive closed-loop forecasting, LSTM showed the lowest errors at 6 h, 24 h, and 48 h horizons, indicating stronger long-horizon stability. Ablation experiments demonstrated that using observed future exogenous variables underestimated forecasting errors, while exogenous-error analysis identified outdoor temperature and solar radiation as the dominant sources of propagated uncertainty. These results highlight the importance of evaluating greenhouse air temperature forecasting under realistic recursive conditions by considering both model stability and future input uncertainty. Full article

Background: Acinetobacter baumannii is an important opportunistic pathogen responsible for healthcare-associated infections, particularly among critically ill, intensive care unit (ICU), and post-surgical patients. The emergence of carbapenem-resistant A. baumannii (CRAB) has become a major therapeutic challenge worldwide because of its multidrug-resistant nature and limited treatment options. Despite the increasing prevalence of CRAB in Pakistan, information regarding the molecular characterization of carbapenem resistance genes among isolates recovered from surgical site infections (SSIs) remains limited. Methods: A cross-sectional study was conducted from November 2023 to February 2024 at Khalifa Gul Nawaz Hospital, Bannu, Khyber Pakhtunkhwa, Pakistan. A total of (n = 118) surgical wound specimens were collected from patients with clinically diagnosed SSIs. A. baumannii isolates were identified using standard biochemical tests and confirmed by 16S rRNA gene sequencing. In vitro antimicrobial susceptibility was determined by Kirby–Bauer disk diffusion according to CLSI 2024 guidelines. In addition, the concentration-dependent inhibition-zone response of imipenem and meropenem against carbapenem-resistant isolates was evaluated using an agar well diffusion assay. Molecular detection of blaNDM-1 and blaOXA-23 was performed by polymerase chain reaction (PCR), and representative amplicons were subjected to Sanger sequencing for further analysis. Results: In the current study, the total number of (n = 118) wound specimens from healthcare-associated patients were processed, in which A. baumannii-positive isolates (n = 23, 19.5%) were documented. Its prevalence was higher in males (69.6%) compared to females, and was strongly associated (78.2%) with elderly patients (aged 51–68 years). In vitro susceptibility testing revealed that 91.3% of isolates harbored multidrug-resistant (MDR) attributes. Antimicrobial susceptibility profiling revealed high levels of multidrug resistance, with 82.6% of isolates resistant to imipenem and meropenem, as well as 100% resistance to aztreonam and gentamicin. Colistin showed the highest activity, with 87.0% of isolates remaining susceptible. In the agar well diffusion assay, measurable inhibition of the carbapenem-resistant isolates by imipenem and meropenem occurred only at higher concentrations. Molecular screening of resistance genes identified blaOXA-23 in 60.9% and blaNDM-1 in 30.4% of isolates. Co-expression of both genes was detected in some (n = 4) isolates. Among the selected MDR isolates included in the gene resistance analysis, imipenem and meropenem resistance were recorded in 9/14 blaOXA-23-positive isolates and 5/7 blaNDM-1-positive isolates. Conclusions: This study substantiates an escalated incidence of carbapenem-resistant, MDR A. baumannii in post-surgical infections. blaOXA-23 were documented as the predominant carbapenemase gene, with co-expression of blaNDM-1 in a substantial proportion of isolates. These findings provide an important phenotypic and molecular characterization of selected carbapenemase genes in healthcare-associated infections, highlighting the exigency for strengthened antimicrobial stewardship and infection control strategies in the hospitals of the region. Full article

Nature-based solutions (NbS) are increasingly recognized as important strategies for advancing agricultural sustainability and supporting the United Nations Sustainable Development Goals (SDGs). Much research has highlighted the biophysical potential of NbS; however, the literature remains fragmented regarding their socio-economic and governance implications. This study synthesizes existing research, identifies major knowledge structures, and develops a forward-looking research agenda. We analyzed 1198 academic articles using Structural Topic Modeling (STM) to identify ten latent topics. These topics were subsequently mapped onto the adapted Agro-Ecotopia model based on two dimensions: Ecosystem State & Control and Governance Logic & Goal Alignment. This approach established an Agro-NbS analytical framework and identified four exploratory scenarios: Green Regulation, Engineered Ecotopia, Vulnerable Wilderness, and Grassroots Resilience. These scenarios characterize diverse pathways of agricultural NbS development, highlighting potential spatial trade-offs and emerging system vulnerabilities associated with social equity. Furthermore, this study extends traditional biophysical assessments by emphasizing the importance of polycentric governance and Traditional Ecological Knowledge (TEK) in addressing complex climate challenges. The findings contribute to the understanding of agricultural Socio-Ecological Systems (SESs) and provide insights for policymakers seeking to promote a more equitable transition toward sustainable agriculture. Full article

Background/Objectives: Carbapenem-resistant Gram-negative (CR-GN) infections are associated with high mortality in intensive care units (ICUs), with limited therapeutic options. We aimed to evaluate microbiological and clinical outcomes associated with cefiderocol in critically ill patients with multidrug-resistant (MDR) Gram-negative infections. Methods: This retrospective, single-center case series included 25 critically ill patients with carbapenem-resistant or multidrug-resistant Gram-negative infections treated with cefiderocol between May 2024 and October 2025 in a mixed ICU of a tertiary hospital in Romania. Microbiological cure was defined as eradication of the designated target pathogen at the end of therapy. Clinical evolution was assessed using the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores, as well as inflammatory biomarkers including white blood cell count (WBC), C-reactive protein (CRP), and procalcitonin (PCT). Results: Klebsiella pneumoniae was the predominant pathogen, identified in 23 of 25 patients (92%), followed by Acinetobacter baumannii (11/25, 44%) and Pseudomonas aeruginosa (6/25, 24%). As multiple pathogens were co-isolated in 16 patients (64%), percentages exceed 100% and are reported as proportions of patients rather than of total isolates. Microbiological cure was achieved in 68% of patients. Mortality was markedly higher in patients without microbiological eradication (88% vs. 35%). Higher baseline APACHE II (HR 1.18, 95% CI 1.02–1.37) and SOFA scores (HR 1.33, 95% CI 1.07–1.65) were associated with increased mortality. Early initiation of cefiderocol (≤10 days from ICU admission) was associated with improved microbiological and clinical outcomes compared to delayed treatment. Reductions in severity scores and inflammatory markers, including C-reactive protein (CRP) and procalcitonin (PCT), were observed during therapy. Pathogen type and combination therapy were not clearly associated with outcomes in this cohort. Conclusions: Cefiderocol was observed in association with clinically relevant rates of microbiological eradication and improvements in clinical parameters in critically ill patients with MDR Gram-negative infections. Outcomes appeared to be primarily determined by baseline disease severity, while earlier initiation of therapy was associated with more favourable microbiological and clinical outcomes, although causality cannot be established given the observational design and absence of a comparator group. These findings are hypothesis-generating and supportive of a potential role for cefiderocol as a salvage option in high-risk ICU populations, pending validation in larger, prospective, controlled studies. Full article